Download c Copyright by Amrita Dhar May 2016

c Copyright by
Amrita Dhar
May 2016
ESSAYS ON CAPITAL FLOWS IN EMERGING
MARKETS
A Dissertation
Presented to
The Faculty of the Department
of Economics
University of Houston
In Partial Fulfillment
Of the Requirements for the Degree of
Doctor of Philosophy
By
Amrita Dhar
May 2016
ESSAYS ON CAPITAL FLOWS IN EMERGING
MARKETS
Amrita Dhar
APPROVED:
David H. Papell, Ph.D.
Committee Chair
Bent E. Sørensen, Ph.D.
Dietrich Vollrath, Ph.D.
Rauli Susmel, Ph.D.
Department of Finance
University of Houston
Steven G. Craig, Ph.D.
Interim Dean, College of Liberal Arts and Social Sciences
Department of Economics
ii
ESSAYS ON CAPITAL FLOWS IN EMERGING
MARKETS
An Abstract of a Dissertation
Presented to
The Faculty of the Department
of Economics
University of Houston
In Partial Fulfillment
Of the Requirements for the Degree of
Doctor of Philosophy
By
Amrita Dhar
May 2016
iii
Abstract
This dissertation comprises two essays. The first essay uses a formal statistical model
to identify episodes of extreme movements in capital flows in emerging markets. In
particular, I employ a three state Markov switching model to characterize periods of
extreme, high, and low net capital flows for a sample of 36 emerging markets using
quarterly data on net private capital flows from 1980:Q1 to 2014:Q4. The model
identifies 8% percent of the total sample as periods of extreme net inflows (“surges”)
and 3 percent of the total sample as extreme net outflows (“flights”). Compared
to the literature, the model identifies fewer episodes as extreme, and the number of
episodes varies substantially across countries. The second essay focus on the dynamic
analysis of the effects of these extreme capital flows (surges and flights) on emerging
markets’ outcomes. The impact of surges and flights on emerging market’s outcomes
is still an open debate in the international finance literature. Using the surges and
flights identified in my first essay, I revisit the question of impact of these extreme
flows in capital on emerging markets’ aggregate output, current account balance, real
and nominal exchange rates. I deal with the potential bias issues from non random
assignment of surges and flights using a propensity score method for time series data
in a local projection framework and estimate the average effects of the extreme flows
on country’s outcomes. The results indicate that surges are contractionary in the
medium horizon whereas the flights do not have any significant effect on output. The
results also shows a deterioration in current account balance. There is an appreciation
of nominal exchange rate but there is no effect on real exchange rate.
iv
Acknowledgements
I am highly indebted to my advisors Prof. David Papell and Prof. Bent Sørensen for
their valuable comments, continued support and motivation throughout the project.
I thank Dr. Dietrich Vollrath, Dr. Kei-Mu Yi, Dr. German Cubas, and Dr. Liliana Varela for their insightful comments and suggestions. I also to thank my fellow
classmates Nadya Abramava, Chon-Kit Ao, Mahbube Asghari, Christopher Biolsi,
Randy Crouch, Ashmita Gupta, Sophia Kazinnik, Volodymir Korsun, Wen Long,
Ryan Ruddy, and Tzu-Yin Hazel Tseng for making the long journey fun and enjoyable and also for being there to help in difficult times during the process. I thank
my friends, especially Anirban Banerjee, Rituparna Roy, and Oindrila Roy for their
encouragement and support. I need to thank all the baristas of the coffee shops in
Houston for supplying the unlimited cups of coffee to keep me awake and finish my
dissertation. Last, but not the least, I am grateful to my family, especially my father
(Parimal Sankar Dhar), my mother (Mamata Dhar), my sister (Paramita Dhar), my
brother-in-law (Anirban Tarafder), my little nephew (Aadit Tarafder), and my parrot
(Mithusmita) for their continued support, encouragement, and patience.
v
to baba
vi
Contents
1 Identification of Extreme Capital Flows in Emerging Markets
1
1.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.2
Literature Review on Capital Flows . . . . . . . . . . . . . . . . . . .
4
1.3
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
1.3.1
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
1.3.2
The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
1.4.1
Markov-Switching: Summary of Results . . . . . . . . . . . .
14
1.4.2
Surges and Flights . . . . . . . . . . . . . . . . . . . . . . . .
15
1.5
Comparison with Existing Methods . . . . . . . . . . . . . . . . . . .
20
1.6
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
1.4
2 Effects of Extreme Capital Flows on Emerging Markets’ Outcomes:
Is it a Blessing or a Curse?
59
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
2.2
Extreme Flows: Identification . . . . . . . . . . . . . . . . . . . . . .
64
2.3
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
2.3.1
Local Projection Framework . . . . . . . . . . . . . . . . . . .
66
2.3.2
Inverse Probability Weighting Estimator . . . . . . . . . . . .
67
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
72
2.4.1
Effect of Surges . . . . . . . . . . . . . . . . . . . . . . . . . .
72
2.4.2
Effect of Flights . . . . . . . . . . . . . . . . . . . . . . . . . .
76
2.4
vii
2.5
Comparison with Existing Methodology . . . . . . . . . . . . . . . . .
78
2.6
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
79
A
108
A.1 Markov-Switching Model Results . . . . . . . . . . . . . . . . . . . . 108
B
111
B.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
viii
List of Figures
1.1
Mean Net Private Capital Flows (in % of GDP): By Country . . . . .
42
1.2
Surges and Flights of Net Capital Flows . . . . . . . . . . . . . . . .
43
1.3
Surges of Net Capital Flows: By Region . . . . . . . . . . . . . . . .
44
1.4
Flights of Net Capital Flows: By Region . . . . . . . . . . . . . . . .
45
1.5
Surge Comparison: By Country . . . . . . . . . . . . . . . . . . . . .
46
1.6
Surge and Flight Episodes . . . . . . . . . . . . . . . . . . . . . . . .
47
1.6
Surge and Flight Episodes (Cont.) . . . . . . . . . . . . . . . . . . . .
48
1.7
Surge and Flight Episodes: Latin and Central American . . . . . . .
49
1.7
Surge and Flight Episodes: Latin and Central American (Cont.) . . .
50
1.7
Surge and Flight Episodes: Latin and Central American (Cont.) . . .
51
1.8
Surge and Flight Episodes: Asia . . . . . . . . . . . . . . . . . . . . .
52
1.8
Surge and Flight Episodes: Asia (Cont.) . . . . . . . . . . . . . . . .
53
1.8
Surge and Flight Episodes: Asia (Cont.) . . . . . . . . . . . . . . . .
54
1.9
Surge and Flight Episodes : Europe and CIS . . . . . . . . . . . . . .
55
1.9
Surge and Flight Episodes: Europe and CIS (Contd.) . . . . . . . . .
56
1.9
Surge and Flight Episodes: Europe and CIS (Contd.) . . . . . . . . .
57
1.10 Surge and Flight Episodes: Other . . . . . . . . . . . . . . . . . . . .
58
ix
List of Tables
1.1
Summary: Net Private Capital Flows (in % of GDP) . . . . . . . . .
33
1.2
Summary of Net Capital Flows (in percentage of GDP): By Country .
34
1.3
Summary: Different Episodes of Net Capital Flows . . . . . . . . . .
35
1.4
Capital Flow Episodes: By Region . . . . . . . . . . . . . . . . . . .
36
1.5
Surge Episodes: By Country . . . . . . . . . . . . . . . . . . . . . . .
37
1.6
Flight Episodes: By Country . . . . . . . . . . . . . . . . . . . . . . .
38
1.7
Surge Comparison Summary (Ghosh et al., 2014) . . . . . . . . . . .
39
1.8
Surge Comparison Summary (Country-wise Threshold) . . . . . . . .
39
1.9
Surge Comparison (Ghosh et al., 2014): By Region . . . . . . . . . .
40
1.10 Surge Comparison by Country . . . . . . . . . . . . . . . . . . . . . .
41
2.1
Annual Surges and Flights by Country . . . . . . . . . . . . . . . . .
81
2.2
Mean comparison between Sub-Populations with Extreme and No Extreme Net Capital Flows . . . . . . . . . . . . . . . . . . . . . . . . .
82
2.3
Effect of Surges on Real GDP Growth: LP-OLS estimates . . . . . .
83
2.4
Effect of Surges on Current Account Balance: LP-OLS estimates . . .
84
2.5
Effect of Surges on Real Exchange Rate: LP-OLS estimates
. . . . .
85
2.6
Effect of Surges on Nominal Exchange Rate: LP-OLS estimates . . .
86
2.7
Effect of Surge on Real GDP Growth: LP-IPWRA estimates . . . . .
87
2.8
Effect of Surge on Current Account Balance: LP-IPWRA estimates .
88
2.9
Effect of Surge on Real Exchange Rate: LP-IPWRA estimates . . . .
89
2.10 Effect of Surge on Nominal Exchange Rate: LP-IPWRA estimates . .
90
2.11 Effect of Flight on Real GDP Growth: LP-OLS estimates . . . . . . .
91
x
2.12 Effect of Flight on Current Account Balance: LP-OLS estimates . . .
92
2.13 Effect of Flight on Real Exchange Rate: LP-OLS estimates . . . . . .
93
2.14 Effect of Flights on Nominal Exchange Rate: LP-OLS estimates . . .
94
2.15 Effect of Flight on Real GDP Growth: LP-IPWRA estimates . . . . .
95
2.16 Effect of Flight on Current Account Balance: LP-IPWRA estimates .
96
2.17 Effect of Flight on Real Exchange Rate: LP-IPWRA estimates . . . .
97
2.18 Effect of Flight on Real Exchange Rate: LP-IPWRA estimates . . . .
98
2.19 Effect of Surges on Real GDP Growth: LP-OLS estimates . . . . . .
99
2.20 Effect of Surges on Real GDP Growth: LP-OLS estimates with Fixed
Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
2.21 Effect of Surge on Real GDP Growth: LP-IPWRA estimates . . . . . 101
A.1 Markov-Switching Result: State Means . . . . . . . . . . . . . . . . . 109
A.2 List of Surge & Flight Episodes by Country from 1980:Q1 to 2014:Q4
110
B.1 Variables Definitions and Data Sources . . . . . . . . . . . . . . . . . 113
B.2 Data Period by Country . . . . . . . . . . . . . . . . . . . . . . . . . 114
xi
Chapter 1
Identification of Extreme Capital
Flows in Emerging Markets
1.1
Introduction
Extreme capital flows have often posed challenges for policy makers in emerging
markets (see Calvo, 1998; Edwards, 2000; Hutchison and Noy, 2006). A sudden large
inflow of capital puts pressure on the domestic currency causing it to appreciate, which
in turn has an adverse effect on the country’s net exports performance. Analogously,
a sudden large outflow of capital causes the domestic currency to depreciate, often
steeply so, and causes inflationary pressure.1 After the Great Recession, interest
rates hit the zero lower bound for many developed countries, causing capital to flow to
countries with higher returns, in particular emerging markets. This led to a resurgence
of interests among academics as well as policy makers in understanding flows of
1
Blanchard et al. (2015b) have showed these flows can have positive effects as well depending on
the nature of flows.
1
capital and their consequences in these economies (see Eichengreen and Gupta, 2014;
Powell, 2013; Ahmed and Zlate, 2013). In December 2012, the International Monetary
Fund (IMF) adopted a new “institutional view” on capital account liberalization and
management of capital flows, and the IMF now acknowledges that regulation of capital
flows is desirable under certain circumstances (see IMF Executive Summary Report,
2012).
There is an extensive literature studying ebbs and flows of capital to emerging
markets going back to Calvo (1998), who documents episodes of “sudden stops,” as
abrupt declines in capital flows using current account data as a proxy for capital
flows. Reinhart and Reinhart (2009) talk about capital flow “bonanzas,” i.e., when
economies experience a huge influx of capital. Part of their analysis involves dating
such episodes for a large sample of countries.2 More recently, Forbes and Warnock
(2012) and Ghosh et al. (2014) study actual capital flows and analyze periods of
extreme flows.
The focus among economists so far has mostly been on analyzing either the causes
or the consequences of extreme episodes. Clearly, identification of “extreme’’ episodes
is crucial for doing such analysis. The existing literature sets ad hoc threshold criteria
for identifying these episodes.3 My objective is to take a step back and focus on rigorous identification of the capital flow episodes using a formal statistical model. This
paper is, to my knowledge, the first study to conduct a formal statistical classification
of extreme capital flow episodes.
I apply a regime switching model, as suggested by Hamilton (1989) for classifying
2
Several other studies look into surges in capital flows. See, for example, Caballero (2012), Powell
and Tavella (2015), and Cardarelli, Elekdag, and Kose (2010).
3
See Reinhart and Reinhart, 2009; Cardarelli, Elekdag, and Kose, 2010; Ghosh et al., 2014; Forbes
and Warnock, 2012.
2
business cycles in U.S. GDP, to characterize capital flow episodes for a sample of
emerging markets. This nonlinear model captures switches between different regimes
and characterizes average flows in each regime. I use a three state Markov-switching
model allowing the mean of the magnitude of the flows to switch between states of
“extreme,” “high,” and “low” flows for each country. The switches between different
states in this model are treated as inherent to the data-generating process, which
allows the extreme episodes to be determined endogenously from the data. Going
forward, I will refer to the “states” and “regimes” interchangeably.
Using quarterly net capital flows, as a percentage of GDP, for a sample of 36
emerging markets from 1980 to 2014, the model identifies a total of 289 quarters of
extreme net inflow periods or “surges” (around 7.8% of the total observations in the
sample and 110 quarters of extreme net outflow episodes, or “flights”, (about 3%
of the total observations in the sample). The number of quarters in extreme states
varies considerably across countries.
I estimate the model for each country separately. I find, the mean of net capital
inflows during the surges is four times the mean in low inflow episodes and around
double the mean of flows during the high inflow regimes. The mean value of net
outflows during flights is six times the value of the net flows of low outflows and
almost three times the value under high outflow regimes.
In comparison to the existing threshold criteria used in the literature, which generally labels the top 20 to 35 percent of the net capital flow distributions as extreme,
the Markov-switching model identifies fewer episodes. Even restricting the threshold
criteria to match the number of surges that the Markov-switching model identifies,
the surge episodes identified under the two methods are not generally the same. This
is because the Markov switching model identifies regimes rather than observations,
3
where the identification of an observation as extreme depends on the pattern of observations nearby in time. Hence, the model may pick half the sample, or just 1 percent
of the sample as extreme episodes depending on the size of the flows relative to the
variation in other periods.
The paper is organized as follows. A brief review of the literature is provided in
Section 1.2. In Section 2.3, I briefly describe the data and explain the methodology.
Section 2.4 analyzes the results. In Section 1.5, I compare the episodes identified
using my methodology with the episodes identified by existing methodologies in the
literature, and finally Section 2.6 concludes.
1.2
Literature Review on Capital Flows
The literature on extreme capital flow goes back to Calvo (1998) who provides a
theoretical analysis of the consequences of “sudden stops” of private capital flows to
emerging market nations. He defines “sudden stops” as sharp unexpected contractions
in net capital flows using country’s current account deficits as a proxy. In a later
study, Calvo, Izquierdo, and Mejia (2004) conduct an empirical analysis of the sudden
stops. The definition of sudden stops they use for the empirical analysis is based on a
threshold approach. In particular, according to their definition, a sudden stop phase
begins when the annual change in the current account deficit is one standard deviation
below its sample mean and ends when it goes above one standard deviation above
the mean. It has to go down two standard deviation below the mean during the
phase in order for the entire phase to be considered as a sudden stop. They further
impose an additional criterion that the episode has to be followed by a contraction
in output. Several studies that followed this used the same definition or modified the
4
definition of extreme events used by Calvo, Izquierdo, and Mejia (2004). While Calvo,
Izquierdo, and Talvi (2006) use the same definition, Calvo, Izquierdo, and Loo-Kung
(2006) consider an additional criterion that these capital account reversals should be
accompanied by an increase in some external aggregate measure of the cost of funds
in order to capture systemic effects as such events are often accompanied by increase
in systemic risk.
Milesi-Ferretti and Razin (2000), Edwards (2007), and Adalet and Eichengreen
(2007) analyze the causes and consequences of large and persistent current account
reversals rather than focusing on short-run fluctuations. According to Milesi-Ferretti
and Razin (2000), large current account reversals have to satisfy three criteria: the
average current account deficit must fall by 2 (or 3)% of GDP between the first three
and second three years; the maximum deficit in the second three years must be no
larger than the minimum deficit in first three years; and the average deficit must fall
by at least a third (as a percentage of GDP) between the first three and second three
years.
Reinhart and Reinhart (2009) analyze the other extreme of capital flow episode
by looking at capital flow bonanzas, i.e., extreme inflows of net capital. They define
bonanzas as periods when net inflows of a country exceed the top twentieth percentile
of the country-specific distribution of net inflows. They also consider the current
account deficit data as a proxy for net inflows data as it was available for a longer
time period and for more countries as opposed to direct capital flows data. They
focus only on net inflows by imposing a non-negativity constraint on the net flows.4
4
They do so to ensure the countries that have mostly outflows of capital on a net basis do not
have any bonanzas.
5
Cardarelli, Elekdag, and Kose (2010) study extreme inflows for a sample of 52
countries and define net private large capital inflows as percentage of GDP for a
country as the ones which are higher than the country-specific rolling historical trend
as well as higher than some region wide threshold where regions include Latin America, Emerging Asia, Emerging Europe and the Commonwealth of Independent States
(CIS), an aggregate group of other emerging market countries and a group of advanced countries. Their region-wide threshold requires net private capital flows (in
percentage of GDP) to be in the 75th percentile of the region wide distribution of the
net capital flows (as percentage of GDP). Unlike earlier studies, they considered actual capital flows data and not current account deficit data as a proxy for the capital
flows. They compute the flows data using various series from the IMF’s International
Financial Statistics, Balance of Payments and World Economic Outlook databases.
Caballero (2012) defines net capital flows bonanzas as episodes when net inflows of
a country grows more than during the normal business cycle and analyze their effect
on the probability of a banking crisis. He decomposes the net flows data into trend
and a cyclical component and define surges as periods when the deviation of the
net inflows from the country-specific long-run trend is higher than one, two or half
standard deviation of the cyclical component by some threshold level.
A recent study by Powell and Tavella (2015) considers two definitions of capital
flows surges using data on gross inflows for a panel of 44 emerging economies from
1980 to 2005 and analyze the effect of surges on the probability of banking crises and
recessions. In their first definition, they define surges as deviations from a sample
wide historical trend whereas in their second definition they define surges as episodes
where gross inflows exceed one standard deviation above the country-specific trend.
Again similar to Cardarelli, Elekdag, and Kose (2010) they compute the historical
6
rolling trend using only past information by employing a Hodrick-Prescott filter.
While Cardarelli, Elekdag, and Kose (2010) and Caballero (2012) focus on the
macroeconomic consequences and policy implications of extreme events in net capital
inflows, a recent study by Ghosh et al. (2014) analyze the plausible determinants
of them for a sample of emerging market economies. Using annual data on actual
capital flows they define “surges” as periods when the net capital flows belong to
the top 30th percentile of the country-specific distribution as well as entire sample’s
distribution of net capital flows.5
As opposed to the above studies that focus either on sharp increases in net inflows (surges or bonanzas) or sharp decreases in net inflows (sudden stops), Forbes
and Warnock (2012) provide a more general study of different possible episodes in
capital flows. They consider actual data on quarterly gross capital flows (inflows and
outflows) and define four possible episodes: surges, stops, flights and retrenchments.
The first two type of episodes are defined based on gross inflows whereas the last two
are defined with regards to gross outflows. They consider gross as opposed to net flows
in order to distinguish between the behavior of domestic and foreign investors. Given
the residency-based definition of capital flows, surges and stops are driven by foreign
investors and flights and retrenchments are driven by domestic investors. Their criterion for identifying these episodes is similar to that followed by Calvo, Izquierdo,
and Mejia (2004). According to their definition, a surge episode begins if the value of
the year over year changes in the four quarter sum of gross capital inflows increases
one standard deviation above the historical mean6 and ends when the value is below
one standard deviation above the historical rolling mean. Also during the episode the
annual change in four quarter sum of the gross inflow has to increase two standard
5
6
The net capital flows considered by Ghosh et al. (2014) are in percentages of GDP.
The historical mean is calculated as a rolling mean over previous four years.
7
deviations above the historical mean at least for a quarter anytime during the period.
Analogously, they define a flight episode using gross outflows.
The existing methodologies used in the literature identify such episodes by either
focusing on the tail of the capital flows distribution by choosing a threshold for top
percentile or by choosing values that deviate by some number of standard deviations
away from the mean. The question that arises regarding such methodologies is how
to choose the value of the threshold? Existing studies take this as given like Reinhart
and Reinhart (2009) or Forbes and Warnock (2012). Ghosh et al. (2014), estimate a
number of quantile regressions to show that large flows as indicated by higher quantiles
of net flows distribution behave qualitatively differently on average from the rest of
the distribution. However, they do not provide any justification of choosing the top
30th percentile as a threshold as opposed to, say, the top 20th or top 35th percentile.
1.3
1.3.1
Methodology
Data
I use quarterly data on net private capital flows from the first quarter of 1980 to
the second quarter of 2014 for a sample of 36 emerging market economies for my
analysis.7 The net private capital flows series is computed using the net financial account excluding government liabilities from the IMF’s Balance of Payment Statistics,
which is similar to the definition followed by Ghosh et al. (2014). The details of the
countries, data period, and the computation of the net flows series used can be found
in Appendix B.1. According to the IMF’s balance of payments (henceforth, BOP)
7
The choice of the countries and data period is restricted mostly by availability and quality of
the data
8
accounting convention, a positive value of the flows indicates net inflows, i.e., capital
flowing into the country on a net basis. Analogously a negative value of the flows
indicates net capital outflow, i.e., capital flowing out of the country on a net basis.
I control for the size of the economy by taking the net flows as a percentage of the
country’s nominal GDP as for larger economies a large flow may not be as much of a
concern relative to a small economy as they may be better able to absorb such large
flows. The nominal GDP data is obtained from the IMF’s World Economic Outlook
database.8
Summary statistics of the data are provided in the Table 1.1. I have a total of
3703 quarterly observations for 36 emerging markets spanning up to 35 years from
1980 to 2014. I group the countries in the sample into four regions: Asia, Latin
and Central America, Europe and CIS (Commonwealth of Independent States) and
Other, where the Other group includes some African and Middle Eastern countries.9
My sample consists of 10 Asian, 11 European and CIS and 12 Latin and Central
American countries, and the “Other’’ group contains 3 countries.
The overall sample mean of the net capital flows as a percentage of GDP is 0.6%.
The East European and CIS countries have a higher mean of around 2.1% of GDP
compared to other regions in the sample. Countries belonging to Asia, Latin and
Central America and other African countries and Israel have means closer to each
other of about 0.4%. Also there is quite a bit of variation in the minimum and
maximum values of the flows across different regions. Figure 1.1 plots the average net
capital flows as a percentage of GDP for individual countries in the sample. Most of
the countries in the sample have on an average net inflows of capital as evident from
8
9
I use the October 2014 version of World Economic Outlook.
In particular, “Other” includes Israel, Morocco and South Africa.
9
the positive values of the mean of net capital flows as percentage of GDP. There are
five countries in the sample (Argentina, Bangladesh, Ecuador, Russia, and Venezuela)
which experienced a net outflow of capital on an average over the entire sample period.
Table 1.2 provides summary statistics of net capital flows in percentage of GDP for
individual countries. Ecuador has the highest net outflow (-30.7%) and Latvia has
the highest net inflow (9.5%) in percentage of GDP relative to other countries in the
sample over the sample period of 35 years.
1.3.2
The Model
The purpose of this paper is to identify periods of extreme flows for a sample of
emerging market economies from the data. As mentioned above, a positive value of
the flows indicates net inflows and a negative value indicates a net outflow of capital.
Thus a country can have periods of extreme inflows or periods of extreme outflows.
The period of extreme inflows would imply a very high “positive” value and a period
of extreme outflows would imply a very high “negative” value of the capital flows.
The question then is how to define such “extreme” values of the flows.
I contribute to the literature by using a rigorous statistical model for identifying
such extreme episodes solely from the data and not, say, looking at the consequences of
the flows. As mentioned earlier, I use the Markov-switching model of Hamilton (1989)
to characterize periods of extreme inflows and outflows. Given the episodic nature
of capital flows to emerging market economies and with the objective of identifying
“exceptionally” large flows, I assume that the net flows in a country follow a regime
switching process with the regimes defining normal times, as well as times of extreme
inflows and outflows, and times of high inflows and outflows. In particular, I allow
10
the means of the absolute values of the net flows to evolve according to a three
state Markov-switching process, thus allowing the dynamics of extreme flows to be
qualitatively distinct from those of non-extreme flows (i.e., high and low flows). The
switches between different states in this model are treated as inherent to the datagenerating process as opposed to the arbitrary threshold model.
The obvious question is why use a three state model as opposed to a simpler two
state model? My objective is to identify periods of net capital flows as percentage
of GDP when they are exceptionally high and not just a period of “high” and “low”
net flows. A two state model would be more likely to have difficulty distinguishing
capital flows episodes that are merely above average from those are truly extreme. I
am interested in identifying periods of extreme flows both in and out of the country.
In addition, I consider absolute values of the series in the Markov-switching model
as opposed to the actual values of the flows to identify the periods of extreme flows.
As I am considering net flows data, which implies values can be negative (indicating
inflows) or positive (indicating outflows), application of a three state regime switching
model in this case identifies only periods of net inflows, net outflows and flows that
are close to zero on a net basis. An alternative would be to allow for six regimes in
the Markov-switching model with actual net flows data. Here, the assumption would
be that the net flows of the data switches between six regimes: very high, high and
low values of net inflows and outflows respectively. However, increasing the number
of regimes make the identification of the states less precise due to the limited amount
of data I have for these emerging market economies. Using the absolute values helps
me to identify the extreme states of the net flows of capital in percentage of GDP
using a relatively parsimonious three state model.
To understand the methodology, let yit denote the absolute values of the net
11
capital flows (in percentage of GDP) for country i at time t. The model postulates
that there exists an unobservable state variable (St ) that can take values 1, 2 and 3 (as
I consider a three state model). When St is equal to 1, the absolute value of the flows
for country i, yit is distributed normally with mean µ1i and variance σi2 N (µ1i , σi2 ).
Similarly when St is equal to 2 and St is equal to 3, the yit is distributed as N (µ2i , σi2 )
and N (µ3i , σi2 ) respectively. I assume that only the mean of the distribution switches
across states. I define the regime with the highest value of the mean as the “extreme”
state.
Formally, I describe the model as:
yit = µSit + it ,
(1.1)
where it ∼ N (0, σi2 )
The unobserved state variable Sit follows a first order Markov process and is
governed by the following transition probabilities:
P r[St = i|St−1 = j] = pij ; i, j ∈ St .
(1.2)
I allow for switches only in the means of the distribution of the net capital
flows keeping the variance unchanged under the different states because I am interested in identifying periods when the countries experience huge flows (inflows or
outflows) compared to their average level. Hence, I do not allow the volatility to
change. The parameters of the model that need to be estimated are µ1 , µ2 , µ3 , σ 2 ,
p11 , p12 , p13 , p21 , p22 , p23 , p31 , p32 , and p33 . Let the parameter vector be denoted by
θ. It is estimated using maximum likelihood estimation using the Matlab Markov
switching package developed by Perlin (2014). The details of estimation method for
12
the parameter vector are explained in Hamilton (1989). I apply a smoothing algorithm for obtaining the state probabilities as proposed by Kim (1994). Also I estimate
the above model for each country separately. Hence, the states of the capital flows
for each countries are determined by country-specific data generating process. This
gives me a parameter vector θi for each country i.
The three state Markov-switching model identifies regimes of extreme, high, and
low flows for each country. The Markov switching model identifies regimes, and not
observations. The model uses the parameter estimates to probabilistically classify
observations in different regimes. If the probability of an observation being in the
extreme regime is the highest among the three regimes, and the value of the net flow
data is positive, I classify it as an extreme inflow.10 Analogously if the probability of
being in the extreme regime is the highest among the three regimes, and the net flow
has a negative sign, it gets classified as an extreme outflow.
In the literature, periods of extreme net inflows and net outflows are usually
termed as surges and flights respectively. In this paper, I also employ that terminology
and call the extreme inflows as “surges” and extreme outflows as “flights”. Similarly
for the other two regimes (high and low flow regimes) I have outflows and inflows
episodes separately. So I get six states for the net flows data using this methodology:
surge, flight, high inflow, high outflow, low inflow, low outflow.
10
I assume there is equal likelihood of an observation being in either of the three states.
13
1.4
1.4.1
Results
Markov-Switching: Summary of Results
Table 1.3 summarizes the different episodes identified by the three state Markovswitching model as described in Section 2.3. As mentioned earlier, the episodes are
identified for each individual country separately. Table 1.3 aggregates across countries
and reports summary statistics on the occurrences of different episodes, average value
of flows, and the average duration of each episode. The model identifies a total of
289 quarters as surges and 110 quarters as flights out of the total 3703 observations.
As evident from Table 1.3, there are relatively few flight episodes compared to surge
episodes. The surges constitute around 8% of the total sample whereas flights are
around 3% of the total sample of observations. This is in line with earlier findings.
Historically, the emerging market economies have experienced more episodes of wildly
fluctuating inflows than outflows (Reinhart and Reinhart, 2009). Comparing the mean
values of the flows across different episodes, it is evident that the extreme episodes do
exhibit really high levels of net capital flows as a percentage of GDP. The surges on
average have net capital flows of 3.4% of GDP and the flights involve net capital flows
of −3.5% of GDP. The high inflows are about 1.5% of GDP and the low inflows are
0.75% of GDP on an average across all countries in the sample. The average flows in
the surge state are about twice the value of average flows in the high inflow state and
about four times the value in the low inflow state. The outflows in extreme periods
are also double the size of high outflows and six times the value in the low outflow
period. The mean flow in a high outflow state is -1.18% of GDP and in low outflow
is -0.56% of GDP.
14
The table also gives the expected duration of each regime measured in quarters which are obtained from the transition probabilities of the three state Markovswitching model. The expected duration, E(D), of a regime, say ‘j’, is calculated
as
E(D) =
1
,
1 − pjj
(1.3)
where pjj is the probability of being in state ‘j’ in period t conditional on being in
state ‘j’ in period t − 1, j=1, 2, 3. Looking at the duration of the different episodes,
it can be seen that the extreme episodes tend to be short-lived compared to the high
and low flows periods. The surges on average tend to last for three quarters and the
flights last for four quarters. The high flows periods are much more persistent lasting
for about four years. The low net flows are a little shorter than the high flows period.
The low inflows last eleven quarters on average and the low outflows last for thirteen
quarters on average.
1.4.2
Surges and Flights
In this section, I analyze the results for extreme flow episodes more closely by focusing
on the surge and flight episodes for the different countries and different region groups.
As mentioned in subsection 1.4.1, the three state Markov-switching model identifies
289 periods of surges and 110 periods of flights of net capital flows. I plot the time
series of surges and flights in Figure 1.2. The top panel of the figure plots the surge
episodes as a percentage of the total number of cross-sectional observations in each
year on the right axis. On the left axis, the graph plots the average net capital flows
as a percentage of GDP for all the countries in the surge episode. The bottom panel
on the other hand plots the flight episodes as a percentage of the total number of
15
cross-sectional observations in each year on the right axis. Again on the left axis, the
figure plots the average net capital flows in percentage of GDP for all the countries
experiencing a flight episode. Although I consider a quarterly frequency of data to
identify the surge and flight episodes for each country, in these graphs I aggregate the
number of episodes to an annual level.
From the top panel in Figure 1.2, we can see that the surge episodes as percentage
of total cross-sectional observations have fluctuated considerably over the period of
analysis. It was around 10% of the total cross-sectional observations in 1980 and then
declined to zero in 1984. There were no surge episodes between 1984 and 1988. From
1989 onwards the surges of capital to emerging markets started to increase and they
continued to increase until the mid-1990s after which again there was a sharp decline
in the surge episodes. The number remained low in the early 2000s.
From 2003 onwards, there was a steady increase in surge episodes in these economies
peaking at 20% in 2007, right before the global financial crisis. This period corresponds to the house price boom in the United States. It was argued by Taylor (2007)
that the Fed followed a loose monetary policy during the period from 2003 to 2006.
Thus these emerging markets attracted foreign investors looking for higher return on
their investments and an opportunity to re-balance their portfolio.
The occurrences of surges again declined sharply in the wake of the global financial
crisis of 2008. With the U.S. interest rate hit the zero lower bound after the financial
crisis capital started surging towards the emerging markets as indicated by the surge
occurrences as percentage of total observations from 2009 onwards. But the recent
talk about exiting the unconventional policy regimes by the Federal Reserve Bank
have triggered capital to flow out of these emerging markets. This can be seen in the
graph as well. In 2013, the “tapering talk” by the Federal Reserve Bank, i.e., when
16
the Federal Reserve was considering about tapering its asset purchases, created a
“taper tantrum” and capital started flowing out of these economies (see (Eichengreen
and Gupta, 2014)) .
The incidence of capital flight is much less than that of capital surges. But again,
the occurrences of flight episodes have also fluctuated over the sample period. Comparing the top and bottom panel, it can be seen that the surge and flight incidences
are mirror images of one another. The periods of surges correspond to periods of low
outflows (no flight). When more countries are experiencing surges, correspondingly
fewer are experiencing flights. Between 1984 and 1989 when there were no episodes
of surges, there were incidences of capital flight. There were around 4% of the total
observations that experienced capital flights during this period. Between 1990 and
1993 there were no countries experiencing any extreme outflow of capital. But the
percentage of observations belonging to surge period increased from 3% to 10%. If
we look at the period of global financial crisis, there was an increase in the incidences
of flight episodes at this time. Again with the reduction in advanced nations’ interest
rates following the crisis, incidences of flight went down in these emerging market
nations as they served as the recipients of the capital that was flowing out of the
advanced nations.
As mentioned in Section 2.3, I divide the entire sample of countries into four
regions: Latin and Central America, Asia, Europe and CIS and Other. Figures 1.3
and 1.4 plot the surge and flight incidences respectively for different countries in
the sample split by the region groups. Again I plot the average net capital flows
as a percentage of GDP in surges and flights respectively on the left axis of the
two figures for all region groups. It can be seen that the episodes identified by the
Markov-switching (henceforth, MS) model match some well known historical events.
17
For example, the MS model identifies periods of surges in Asian countries before
the Asian Financial Crisis and periods of flights in the late 1990s and early 2000s.
For Latin and Central American countries, the MS model identifies high incidence
of flights between 2003 and 2006 which corresponds to the period after the South
American economic crisis when Argentina, Brazil and Uruguay experienced some
economic disturbances. Also the MS model is able to identify the capital outflows
from Latin American countries after the Latin American Debt Crisis of the 1980’s as
evident from the top left panel of Figures 1.4.
Comparing across all four groups in Figure 1.3, it can be seen that the incidences
of surges are not synchronized across all region groups. For example, Latin and
Central American countries experienced surges more in the late 1990s whereas there
were high incidence of surges for Asian countries in the early 1990s. The European
and CIS countries experienced more surges in the mid 2000s. The Asian, European
and Other countries experienced a steady increase in the incidences of surges in the
2000s until the onset of the Great Recession. But the Latin and Central American
countries fluctuated a lot in this period. Looking at Figure 1.4, we see that the global
financial crisis did not affect all the regions to the same extent. There was a sharp
increase in the incidences of flight episodes in the European region relative to other
groups. Asia also had an increase in the percentages of flight episodes relative to total
observations in the region but it was much less compared to what the Asian economies
had experienced after the Asian financial crisis in 1997-1998. In the Latin American
countries, however, the flight incidences actually went down during the crisis.
In order to understand further how well the MS approach identifies the extreme
episodes in the different countries, I look at some of the individual countries in the
sample. Figure 1.6 plots the net capital flows of eight countries: Argentina, Mexico,
18
Thailand, Indonesia, Brazil, Russia, India, and South Africa.
For Argentina, the MS model identifies periods of flights in the late 1980s, i.e.,
after the Latin American debt crisis, and a few periods of flights after the depression
in the country. For Mexico, the MS approach identifies periods of surges before the
Mexican Peso Crisis of 1994. Also there is some evidence of a surge in capital in 2009,
when the U.S. interest rate hit the zero lower bound. The graph shows that there
was some capital leaving the Mexican economy after the devaluation of the Mexican
peso in December 1994. However, the MS model results show that this was not high
enough to be part of a different data generating process and hence, these periods
are not classified as extreme capital outflows. For Indonesia and Thailand, again the
MS model captures episodes of flights around the period of Asian Financial Crisis of
1997-1998. For Indonesia, the capital flight episode started in 1997:Q4 and continued
till 2001:Q3.
Next, I analyze the results for four major emerging market economies, Brazil,
Russia, India and South Africa, which are also part of the BRICS group.11 According
to the MS model, Brazil experienced only one period of surge in the second quarter
of 1994. Only India and South Africa experienced surges in capital flows in the post
Global Financial Crisis period when the Fed lowered the interest rates in U.S. Also
the results indicate that the “tapering talk” by the Fed in the summer of 2013 did not
lead to a significant outflow of capital for any of these countries. Russia experienced
two episodes of flights, one in the third quarter of 2000 and the other in the fourth
quarter of 2008. It is experiencing outflows of capital in recent years, however, these
outflows have not been large enough to be classified as distinct flight episodes by the
MS model.
11
BRICS is an acronym for Brazil, Russia, India, China and South Africa that refer to an association of these major emerging market economies.
19
1.5
Comparison with Existing Methods
In this section, I compare the results that I get from using the three state Markovswitching model with the methods used in the current literature. As mentioned earlier, the literature on analysis of net capital flows’ tends to rely on arbitrary threshold
approaches to identify periods of extreme flows. The study that comes closest to my
analysis is that of Ghosh et al. (2014) as the data on net capital flows I use is very
similar to theirs.12 also identify periods of extreme flows using actual flows data.
However, their study analyzes gross flows as opposed to net flows. Also the criterion
they use for identifying the extreme flows is based on a threshold in the standard
deviation of the flows, i.e., it is more of a volatility threshold. However, the sample
of countries and the frequency of the data I consider is different. In their paper, they
identify periods of net inflows that are abnormally high using data on annual net
private capital inflows as a percentage of GDP for a sample for 56 emerging market
economies from 1980 to 2008. According to their methodology a period (measured in
years) is considered to be a surge episode if it belongs to the top 30th percentile of
country’s own distribution of net capital inflows as well as in the top 30th percentile
of the entire sample distribution of net capital flows. Since the data and the sample
of countries I consider do not exactly match with that used by Ghosh et al. (2014),
in order to compare my results to theirs, I apply their methodology to my data to
identify the surge periods.13
Table 1.7 provides a comparison of surge episodes between the Markov-switching
model (henceforth, MS) and the threshold approach used by Ghosh et al. (2014) by
12
Forbes and Warnock (2012)
Reinhart and Reinhart (2009) also use a threshold approach to date episodes of extreme capital
inflows, which they refer to as bonanzas. However, they used current account data as a proxy for
net capital flows data rather than using actual net flows data. They considered a threshold of 20th
percentile for all countries to identify the capital inflow bonanzas.
13
20
aggregating across all countries in the sample. It should be noted that their method
identifies only surge episodes as they consider the top 30th percentile of net flows
of capital where a positive value indicates a net inflow of capital. The model that
I use, however, characterizes simultaneously both periods of extreme inflows and
outflows on a net basis. But for comparing with the threshold approach used by
Ghosh et al. (2014), I consider only the surge episodes identified by the Markovswitching model. Table 1.7 reports the surge episodes identified by the Markovswitching model as the horizontal variable and the surges identified using the 30th
percentile threshold approach as the vertical variable. As discussed in section 1.4.1,
the MS model identifies only 7.8% of the total observations in the sample as surges (a
total of 289 surge episodes).14 Table 1.7 shows that the threshold approach identifies
849 quarters as surge episodes (22.3% of the total sample) for the same sample of
countries which is almost thrice the number identified by the MS approach. There
are 263 surge episodes that are identified under both the the methods, which is
around 91% of the total episodes identified under the MS approach. However, 26
episodes out of the 289 episodes identified by the MS approach are not identified by
the threshold approach. The correlation between the two methods is 0.47. It should,
however, be noted that the MS algorithm classifies surges for each country separately
whereas the threshold approach followed by Ghosh et al. (2014) involves a countryspecific threshold as well as a sample-wide threshold. Due to this there can be some
differences in the identification of the surge episodes between the two methods.
In their paper, Ghosh et al. (2014) also consider a country-specific threshold of
the top 30th percentile as a robustness check. I also make a comparison of my results
with the country-specific cutoff for net capital flows as percentage of GDP. The results
14
The episodes are measured in number of quarters
21
of the comparison of the two methods are summarized in Table 1.8. By changing the
cutoff to a country-specific distribution of net capital flows, the number of surges
increases to 1115. This is not surprising as now around 30 percent of the total sample
will be regarded as surges. Out of the 289 surges identified by the MS model, now 272
(94%) of them matches with the episodes identified by the country-specific threshold
criterion.
Although for the overall sample, I find a high match rate for the surge episodes
between the two algorithms, however, there seems to be quite a bit of heterogeneity
in the match rates across different countries. This can be seen from Figure 1.5 and
Table 1.5. The threshold algorithm used by Ghosh et al. (2014) identifies on average
more periods as surges compared to the MS algorithm. There are two countries in
the sample, Hungary and Sri Lanka, for which the MS model identifies more periods
of surges compared to the threshold approach. For most of the countries the MS
algorithm identifies much fewer surge episodes than the threshold approach. For
example Argentina, the MS model identifies only one quarter as the surge period
whereas the threshold approach identifies 13 quarters as surges. For Albania and
South Africa, however, the number of surges identified by the two methodologies are
close. The threshold approach identifies 24 quarters as surges for Albania and 23
quarters as surges for South Africa. The MS model identifies 21 quarters and 20
quarters as surges for Albania and South Africa respectively. For South Africa all
these 20 periods are identified by the threshold approach as well as MS model as
evident from the third column of Table 1.5. However, for Albania 16 out of the 21
quarters are identified as surges under the threshold approach.
For a better understanding of how the surge episodes vary across the two methods,
consider the Figures 1.7, 1.8, 1.9 and 1.10 In these graphs I plot the net capital flows
22
of each country as a percentage of GDP from 1980:Q1 to 2014:Q2 and highlight the
periods of surges and flights as identified by the three state MS model.15 The gray
shaded bars in the graphs indicate the periods of surges and the green shaded areas
highlight the periods of flights as identified by the MS model. The red line represent
the threshold criterion used by Ghosh et al. (2014). Recall that according to their
algorithm a surge episode for a country not only needs to be in the top 30th percentile
of its own distribution of net capital flows (in percentage of GDP) but also has to
belong to the top 30th percentile of the entire sample’s distribution of net capital flows
(in percentage of GDP). This implies for the countries with a top 30th percentile value
higher than the sample wide value of the top 30th percentile, the effective criterion
for being qualified as surge episode will be its own country-specific 30th percentile.
Similarly for countries with a lower value of the top 30th percentile of net capital flows
relative to the sample wide value, the effective criterion for identifying surges is the
top 30th percentile of the entire sample. So the red line in the graph represents the
effective criterion (either the country-specific or the sample wide top 30th percentile
value) to identify surge episodes used by Ghosh et al. (2014). I plot these graphs
separately for all the 36 countries in the sample. Again I divide the sample into four
groups according to the region: Latin and Central America, Asia, Europe and CIS,
and the other countries are grouped into “Other.”
By looking at the graphs in Figures 1.7, 1.8, 1.9 and 1.10, it is evident that the
MS algorithm picks up periods as surges and flights in which the net capital flows
as percentage of GDP is highly positive and highly negative respectively relative to
the other periods in the sample which was the objective of this paper. There is
15
As mentioned earlier, data for all countries are not available from 1980:Q1. So I consider the
starting period for the different countries as the period when the data is available continuously. For
most of the countries the sample period is until 2014:Q2.
23
quite a bit of variation in the duration of these episodes across countries. For a lot of
countries, the surges and the flight tend to be short-lived, by lasting only for a quarter
(for example Brazil, Colombia, and Korea). On the other hand, in countries like Sri
Lanka, Hungary, Latvia and Ukraine the extreme episodes tend to be relatively more
persistent (see Figures 1.8h, 1.9f, 1.9e, 1.9k).
Ecuador, Russia, Korea, Poland, and Indonesia do not have any surges in net
capital flows but rather only flight episodes. But based on the threshold criteria
there are surges in these countries as depicted in Figures 1.9i, 1.8d, 1.9g, 1.8c. If
we look at the graph for Argentina in Figure 1.7a, we find there is an exceptionally
huge inflow in the third quarter of 1993 which gets tagged as a surge episode under
the MS model but the threshold approach identifies lot of quarters between 1993:Q1
and 1999:Q4 as surges. We get a similar comparison for Brazil as well. For Brazil,
the net capital flows in percentage of GDP went up to 8% in the first quarter of
1994 as evident from Figure 1.7b. Again, the MS model identifies only this quarter
as an extreme inflow episode. However, under the threshold approach a lot of other
observations get identified which have net flows values in percentage of GDP much
lower than that of the first quarter of 1994.
In some cases, the threshold criteria picks up episodes that are small fluctuations
in net capital flows. For Morocco, on the other hand the two algorithms give exactly
the same results (see Figure 1.10b). For Bangladesh, none of the periods qualify as
surges based on the threshold criteria. The effective threshold criteria is the samplewide criteria for Bangladesh. However, the MS model identifies 6 periods as surges.
This shows the importance of having country-wise criteria for identification of extreme
episodes.
One of the plausible reasons why the two models give such different results is
24
due to the way the MS algorithm works. The MS model assumes the unobserved
state variable follows a first order Markov process. This implies that the evolution
of the state variable depends on the last period’s state. Thus the probability that an
observation is in an extreme state today depends on what the state was in the last
period. However, the threshold methodology for identifying extreme episodes does
not consider such dependence. It only uses rank information in the net capital flows
distribution for classifying surges.
As mentioned above, the threshold approach identifies around 23% of the total
sample of observations as surges whereas the MS method identifies only 7.8% as
surges. By changing the threshold level to 12.5% from 30%, then we get the same
number of surges as the MS method, i.e., 289 quarters. However, only 164 out of these
289 observations are identified by the MS method which implies that by tightening
the threshold criteria, although we are able to match the surge episodes under the two
methods quantitatively, but qualitatively, they are still very different. The correlation
between the two method is 0.53.
1.6
Conclusion
This paper provides a formal methodology for identifying periods of extreme capital
flows in emerging markets. Rather than relying on arbitrary thresholds, as previous
studies have done, this paper employs a rigorous statistical model, namely, Hamilton
(1989)’s Markov switching model, to classify capital flow episodes entirely from the
data.
Using quarterly data on net private flows for a sample of 36 emerging market
economies over a period of 35 years from 1980 to 2014, the model identifies 7.8% of
25
total observations as surges (extreme inflows) and 3% of total sample observations
as flights (extreme outflows). The model is able to identify the states distinctly with
the means of each state being statistically significant for each country in the sample.
These extreme episodes tend to be short-lived compared to the other states of the
net flows. However, there is a lot of variation in incidences of surges and duration of
these extreme flows across countries.
In comparison to the exogenous threshold criteria for identifying surges used in the
literature, the Markov-switching model identifies a much lower incidence of surges.
Even restricting the threshold criteria to match the number of surges that the Markovswitching model identifies, I find the surge episodes identified under the two methods
differ considerably.
Identification of the extreme episodes are crucial for conducting analyses on these
extreme episodes in capital flows. Given the difference between the classification of
episodes using a formal statistical model and using arbitrary threshold criteria is
non-trivial, it may have important implications in the analyses of cause and effects of
these episodes. This in turn may affect the policy implications based on the analysis of
extreme events in capital flows. This paper does not analyze the determinants of these
episodes or their consequences on the country’s economies. The next step would be to
take these episodes identified using the formal statistical methodology proposed in this
paper, and analyze their determinants and consequences on countries’s economies.
26
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32
Table 1.1: Summary: Net Private Capital Flows (in % of GDP)
Region Groups
Asia
Europe & CIS
Latin & Central America
Other
Total
Mean
No of
Countries
10
11
12
3
36
N
Mean
Sd
Min
Max
1225
984
1176
318
3703
0.5
1.0
0.4
0.3
0.6
1.2
2.0
1.9
1.2
1.7
-5.4
-11.0
-30.7
-3.8
-30.7
7.6
9.5
8.3
3.7
9.5
Notes: The table reports summary of the net private capital flows (in percentage of GDP) for total
countries in the sample for the period 1980:Q1 to 2014:Q4 (or the latest available data) split by the
region.
33
Table 1.2: Summary of Net Capital Flows (in percentage of GDP): By Country
country
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
Total
N
Mean
Sd
Min
Max
78
137
135
74
137
94
74
77
85
62
137
99
136
134
137
137
84
75
137
44
64
90
136
52
92
137
117
94
82
137
137
136
122
82
79
73
3703
1.1
-0.1
-0.1
1.1
0.4
0.7
0.7
1.3
-0.4
0.5
0.7
1.6
0.5
0.2
0.1
0.1
2.0
1.5
0.5
0.6
0.7
1.3
0.3
0.5
1.4
0.6
0.7
1.4
-0.4
0.3
0.6
0.8
0.8
0.5
-1.3
1.7
0.6
1.5
1.4
0.3
1.5
1.3
1.5
0.7
1.5
3.5
1.8
1.3
2.1
0.5
1.1
1.4
1.0
3.3
2.2
1.2
0.7
0.5
2.7
0.6
1.6
1.5
1.4
1.7
1.6
1.9
0.9
0.9
1.9
1.2
2.0
2.0
1.8
1.7
-2.5
-5.0
-0.9
-1.9
-3.8
-4.0
-0.7
-1.4
-30.7
-6.8
-4.7
-3.4
-0.5
-4.1
-3.8
-4.6
-7.1
-3.8
-2.4
-0.7
0.2
-11.6
-1.2
-3.6
-1.8
-3.4
-11.0
-1.3
-7.7
-2.1
-2.1
-5.4
-3.9
-5.9
-6.1
-1.6
-30.7
6.6
7.4
0.9
5.0
8.0
4.4
3.1
7.5
2.6
4.5
4.2
8.0
2.7
2.1
3.7
2.8
9.5
7.1
3.2
2.5
3.2
7.1
1.8
5.1
8.3
4.5
3.6
6.2
4.2
3.3
2.9
4.9
3.7
6.4
4.4
7.6
9.5
Notes: The table reports summary of net private capital flows (in percentage of GDP) for different
countries in the sample for the period 1980:Q1 to 2014:Q4 (or the latest available data)
34
Table 1.3: Summary: Different Episodes of Net Capital Flows
Occurrence
Net Flows
Duration
# of Quarters
Mean
Std. Dev.
Mean
Std. Dev.
Surges
Flights
289
110
3.24
-3.30
1.78
3.33
2.90
4.19
2.96
5.26
High Inflows
High Outflows
939
330
1.49
-1.18
0.98
0.89
14.50
13.32
17.97
15.60
Low Inflows
Low Outflows
1,327
708
0.75
-0.56
0.64
0.57
16.68
17.85
11.73
12.42
Total
3703
0.59
1.68
Notes: The table reports the summary of episodes of the net capital flows in percentage of GDP
for a total of 36 emerging market countries for period 1980:Q1 to 2014:Q4 as identified by a three
state Markov-switching model using the absolute values of the net capital flows for each country and
allowing in switches in mean only. The first column reports the total number of quarters falling in
each of the six episodes. The second column reports mean value of the net capital flows (in percent
of GDP) for each of the episodes and the last column reports the average duration of the different
episodes (in quarters)
35
Table 1.4: Capital Flow Episodes: By Region
Asia
Europe & CIS
Latin & Central America
Other
Total
Obs
1225
984
1176
318
3703
Surges
74
124
54
37
289
Surge%
6.0
12.6
4.6
11.6
100
Flights
47
16
38
9
110
Flight%
3.8
1.6
3.2
2.8
100
Notes : The table reports the summary of episodes of the net capital flows in percentage of GDP
for a total of 36 emerging market countries for period 1980:Q1 to 2014:Q4 split by different region
groups as identified by a three state Markov switching model using the absolute values of the net
capital flows for each country and allowing in switches in mean only. The first column reports the
total number of quarters falling in each of the six episodes. The second column reports mean value
of the net capital flows (in percent of GDP) for each of the episodes and the last column reports
the average duration of the different episodes (in quarters)
36
Table 1.5: Surge Episodes: By Country
Country
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
Total
N
78
137
135
74
137
94
74
77
85
62
137
99
136
134
137
137
84
75
137
44
64
90
136
52
92
137
117
94
82
137
137
136
122
82
79
73
3703
Mean
Net
Capital
Flow in %
of GDP
Sum
Surges
Surge(%)
1.08
-0.10
-0.09
1.15
0.42
0.68
0.70
1.29
-0.37
0.47
0.68
1.57
0.53
0.18
0.14
0.13
1.99
1.49
0.52
0.57
0.73
1.32
0.27
0.49
1.36
0.57
0.73
1.43
-0.40
0.34
0.61
0.75
0.81
0.46
-1.27
1.69
0.59
21
1
6
15
1
7
2
1
0
3
6
35
3
0
12
0
14
5
9
5
1
14
10
3
6
5
0
3
0
20
42
2
17
13
2
5
289
26.9
0.7
4.4
20.3
0.7
7.4
2.7
1.3
0.0
4.8
4.4
35.4
2.2
0.0
8.8
0.0
16.7
6.7
6.6
11.4
1.6
15.6
7.4
5.8
6.5
3.6
0.0
3.2
0.0
14.6
30.7
1.5
13.9
15.9
2.5
6.8
7.8
Mean
Net
Capital
Flow in %
of GDP
(Surge)
2.49
7.42
0.67
3.51
8.03
3.55
2.85
7.53
Duration
1.0
2.2
1.2
1.4
1.0
1.0
1.0
1.0
4.16
3.50
3.63
2.36
1.3
1.0
4.7
3.0
3.04
1.3
6.15
5.90
2.54
1.97
3.20
5.42
1.49
4.28
5.35
3.97
14.7
1.0
1.0
1.0
1.0
1.6
1.6
1.3
1.2
1.2
5.87
1.0
1.99
1.62
4.80
2.75
3.25
3.69
6.31
3.24
2.1
3.2
1.0
2.0
4.0
2.0
4.6
2.9
Notes : The Table reports summary of surge episodes of the net capital flows (as percentage of GDP) for different
countries in the sample as identified by a three state Markov switching model using absolute values of the series. The
first column reports the total number of observations, the second column reports the total number of surge episodes
measured in quarters. The third column gives the surges as percentage of total observations in the country. The
fourth column reports mean value of the net capital flows (in % of GDP) in surge and the last column reports the
average duration of the surge episodes (in quarters).
37
Table 1.6: Flight Episodes: By Country
Country
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
Total
N
78
137
135
74
137
94
74
77
85
62
137
99
136
134
137
137
84
75
137
44
64
90
136
52
92
137
117
94
82
137
137
136
122
82
79
73
3703
Mean
Net
Capital
Flow in %
of GDP
Sum
Flights
Flight(%)
1.08
-0.10
-0.09
1.15
0.42
0.68
0.70
1.29
-0.37
0.47
0.68
1.57
0.53
0.18
0.14
0.13
1.99
1.49
0.52
0.57
0.73
1.32
0.27
0.49
1.36
0.57
0.73
1.43
-0.40
0.34
0.61
0.75
0.81
0.46
-1.27
1.69
0.59
0
8
18
0
0
2
0
0
1
3
1
3
0
16
6
2
3
0
2
0
0
2
4
1
0
1
1
0
2
3
3
3
2
5
18
0
110
0.0
5.8
13.3
0.0
0.0
2.1
0.0
0.0
1.2
4.8
0.7
3.0
0.0
11.9
4.4
1.5
3.6
0.0
1.5
0.0
0.0
2.2
2.9
1.9
0.0
0.7
0.9
0.0
2.4
2.2
2.2
2.2
1.6
6.1
22.8
0.0
3.0
Mean
Net
Capital
Flow in %
of GDP
(Flight)
Duration
-3.69
-0.61
2.2
1.2
-3.84
1.0
-30.72
-4.55
-4.73
-2.64
1.0
1.3
1.0
4.7
-2.17
-2.98
-4.39
-5.84
15.7
1.3
1.0
14.7
-2.32
1.0
-7.96
-1.16
-3.57
1.6
1.6
1.3
-3.41
-11.02
1.2
1.0
-6.71
-1.77
-1.60
-4.89
-3.14
-4.08
-3.95
1.0
2.1
3.2
1.0
2.0
4.0
2.0
-3.30
4.2
Notes : The table reports summary of flight episodes of the net capital flows as percentage of GDP for different
countries in the sample as identified by a three state Markov switching model using absolute values of the series.
The first column reports the total number of observations, the second column reports the total number of flight
episodes measured in quarters. The third column gives the flights as percentage of total observations in the country.
The fourth column reports mean value of the net capital flows (as percentage of GDP) in flight and the last column
reports the average duration of the Flight episodes (in quarters).
38
Table 1.7: Surge Comparison Summary (Ghosh et al., 2014)
MS (3 State)
No Surge
Surge
Total
No Surge
Ghosh et al.(2014)
Surge
Total
2828
26
2854
586
263
849
3414
289
3703
Notes : The table compares the surge episodes identified by the three state Markov-switching
model with absolute values of the net flows as percentage of GDP and the surge episodes identified
by Ghosh et al. (2014). According to their criterion, a Surge for a country is defined as the quarter
in which the net capital flow (as percentage of GDP) has to be in the top 30th percentile of the
country’s own distribution as well as in the top 30th perecentile of the entire sample distribution of
net capital flows.
Table 1.8: Surge Comparison Summary (Country-wise Threshold)
Surge(MS)
0
1
Total
30 Percentile (Country-wise)
0
1
2571
17
2588
843
272
1115
Total
3414
289
3703
Notes: The table compares the surge episodes identified by the three state Markov-switching
model with absolute values of the net flows and surge episodes based on 30th percentile threshold
(country-wise). According to this criterion, a surge for a country is defined as the quarter in which
the net capital flow (as percentage of GDP) has to be in the top 30th percentile of the country’s
own distribution of net capital flows.
39
Table 1.9: Surge Comparison (Ghosh et al., 2014): By Region
Asia
Europe & CIS
Latin & Central America
Other
Total
Obs
MS
1225
984
1176
318
3703
74
124
54
37
289
Ghosh et al.
(2014)
204
289
293
63
849
Both
61
111
54
37
263
Notes: The table compares the surge episodes identified by the three state Markov-switching model
with absolute values of the net flows as percentage of GDP and the surge episodes identified by
Ghosh et al.(2014) across different region groups. According to their criterion, a Surge for a country
is defined as the quarter in which the net capital flow (in percentage of GDP) has to be in the top
30th percentile of the country’s own distribution as well as in the top 30th perecentile of the entire
sample distribution of net capital flows. Third column gives the number of periods identified as
surges under both MS and threshold model
40
Table 1.10: Surge Comparison by Country
Country
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
Total
MS
21
1
6
15
1
7
2
1
0
3
6
35
3
0
12
0
14
5
9
5
1
14
10
3
6
5
0
3
0
20
42
2
17
13
2
5
289
Sum
Ghosh et al. (2014)
24
18
0
22
34
28
22
24
11
19
41
30
15
14
33
12
26
22
42
6
10
27
9
15
27
41
36
28
16
24
40
41
36
25
9
22
849
Both
16
1
0
15
1
7
2
1
0
3
6
27
3
0
12
0
14
5
9
5
1
14
9
3
6
5
0
3
0
20
36
2
17
13
2
5
263
Notes: The table compares the Surge episodes identified by the three state Markov-switching model
with absolute values of the net flows and the Surge episodes identified by Ghosh et al.(2014) for
each countries. According to their criterion, a Surge for a country is defined as the quarter in which
the net capital flow has to be in the top 30 percentile of the country’s own distribution as well as
in the top 30 perecentile of the entire sample distribution of net capital flows.
41
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
Average Net Capital Flows (in % of GDP)
-1
1
-2
0
2
Figure 1.1: Mean Net Private Capital Flows (in % of GDP): By Country
Source: IMF Balance of Payments Statistics and author’s calculation
42
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
0 5 10 15 20 25
Surge %
Surge
1980
Ave Net Flows (in % of GDP)
1 2 3 4 5 6
Figure 1.2: Surges and Flights of Net Capital Flows
year
Surge %
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
0 2 4 6 8
Flight %
Flight
1980
Ave Net Flows (in % of GDP)
-8 -6 -4 -2 0
Ave Net Flows (in % of GDP)
year
Ave Net Flows (in % of GDP)
Flight %
Notes: The top panel of the figure plots the surge episode as percentage of total sample observations
on the left axis and the average net capital flows as percentage of GDP in the surge state for the
entire sample on the right axis. The bottom panel plots the flight episodes as percentage of total
sample observations on the left axis and the average net capital flows as percentage of GDP in the
flight state on the right axis. The red line gives the total number of surge episodes in percentage of
total number of sample observations. The period of data considered is from 1980:Q1 - 2014:Q4 for
most of the countries or the latest available data as of 2014:Q2.
43
Figure 1.3: Surges of Net Capital Flows: By Region
.8
.4
.6
Surge %
.2
2014
0
2012
2014
year
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
0
.5
1
Surge %
Ave Net Flows (in % of GDP)
2 2.5 3 3.5
1.5
.5
1
Surge %
0
2014
2012
2010
2008
1.5
Other
2006
2004
2002
2000
1998
2010
Surge %
1996
2012
2008
2006
2004
2002
Surge %
1994
1992
1990
1988
2000
Ave Net Flows (in % of GDP)
Ave Net Flows (in % of GDP)
1 2 3 4 5
1986
1998
year
Ave Net Flows (in % of GDP)
Europe & CIS
1984
2010
year
1996
1994
1992
1990
1988
1986
1984
1982
1980
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
0
.1
.2
.3
Surge %
.4
Ave Net Flows (in % of GDP)
1 2 3 4 5
Asia
Ave Net Flows (in % of GDP)
3
4
5
6
7
Latin & Central America
year
Ave Net Flows (in % of GDP)
Ave Net Flows (in % of GDP)
Surge %
Surge %
Notes: The figure plots the average net capital flows in percentage of GDP for the surge period
across different countries in the sample split by region: Latin and Central American, Asia, Europe
and CIS and Other which is represented by the blue bars in the figure. The red line gives the total
number of surge episodes in percentage of total number of observations in the group. The period of
data considered is from 1980:Q1 - 2014:Q4 for most of the countries or the latest available data as
of 2014:Q2.
44
Figure 1.4: Flights of Net Capital Flows: By Region
year
.8
.4
.6
Flight %
.2
2014
0
2012
2014
.5
.4
.2 .3
Flight %
.1
0
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
2014
2012
2010
2008
0
.2
.4
Flight %
.6
Ave Net Flows (in % of GDP)
-4 -3.5 -3 -2.5 -2 -1.5
Other
2006
2004
2002
2000
1998
2010
Flight %
1996
2012
2008
2006
2004
2002
Flight %
1994
1992
1990
1988
2000
Ave Net Flows (in % of GDP)
Ave Net Flows (in % of GDP)
-12 -10 -8 -6 -4 -2
1986
1998
year
Ave Net Flows (in % of GDP)
Europe & CIS
1984
2010
year
1996
1994
1992
1990
1988
1986
1984
1982
1980
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
0
.1
.2
.3
Flight %
.4
Ave Net Flows (in % of GDP)
-8
-6
-4
-2
0
Asia
Ave Net Flows (in % of GDP)
-30
-20
-10
0
Latin & Central America
year
Ave Net Flows (in % of GDP)
Ave Net Flows (in % of GDP)
Flight %
Flight %
Notes: The figure plots the average net capital flows in percentage of GDP for the flight period
across different countries in the sample split by region: Latin and Central American, Asia, Europe
and CIS and Other which is represented by the blue bars in the figure. The red line gives the total
number of flight episodes in percentage of total number of observations in the group. The period of
data considered is from 1980:Q1 - 2014:Q4 for most of the countries or the latest available data as
of 2014:Q2.
45
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
0
10
20
30
40
Figure 1.5: Surge Comparison: By Country
sum of Surges (MS)
sum of Surges (Ghosh et al. 2014)
Notes: The figure plots total surge episodes identified by Mark-switching model and Ghosh et al.
(2014) for individual countries.
46
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
2014q1
2011q3
2009q1
2006q3
2004q1
2001q3
1999q1
1996q3
1994q1
1991q3
1989q1
1986q3
1984q1
1981q3
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
(a)
Indonesia
Time
(c)
47
-2 -1
0
1
2
3
Net Capital Flows (in % of GDP)
5
-5
0
10
Net Capital Flows (in % of GDP)
Argentina
-5
0
5
Net Capital Flows (in % of GDP)
-2
0
-4
2
Net Capital Flows (in % of GDP)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
Figure 1.6: Surge and Flight Episodes
Mexico
Time
Time
(b)
Thailand
Time
(d)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP).
1993q3
Time
(g)
48
0
1
2
3
Russia
-1
(e)
-2
-10
0
5
-5
Net Capital Flows (in % of GDP)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
-1
0
1
2
3
Net Capital Flows (in % of GDP)
5
-5
0
10
Net Capital Flows (in % of GDP)
Brazil
Time
Time
SouthAfrica
Net Capital Flows (in % of GDP)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
2014q3
2013q1
2011q3
2010q1
2008q3
2007q1
2005q3
2004q1
2002q3
2001q1
1999q3
1998q1
1996q3
1995q1
Figure 1.6: Surge and Flight Episodes (Cont.)
India
(f)
Time
(h)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP).
Figure 1.7: Surge and Flight Episodes: Latin and Central American
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
-5
0
5
10
Net Capital Flows (in % of GDP)
Brazil
5
-5
0
10
Net Capital Flows (in % of GDP)
Argentina
Time
Time
(a)
(b)
Time
2014q1
2012q3
2009q3
2011q1
2008q1
2006q3
2003q3
2005q1
2002q1
2000q3
1999q1
1997q3
1996q1
1994q3
1993q1
1991q3
2014q1
2012q3
2009q3
2011q1
2008q1
2006q3
2005q1
2003q3
2002q1
2000q3
1999q1
1997q3
1996q1
-4
-2
0
2
4
Net Capital Flows (in % of GDP)
Chile
-1
0
1
2
3
Net Capital Flows (in % of GDP)
Colombia
Time
(c)
(d)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
49
Figure 1.7: Surge and Flight Episodes: Latin and Central American (Cont.)
2014q1
2013q1
2011q1
2012q1
2010q1
2009q1
2007q1
2008q1
2006q1
2004q1
2005q1
2002q1
2003q1
2001q1
1999q1
Time
2000q1
2014q1
2012q3
2009q3
2011q1
2006q3
2008q1
2005q1
2002q1
2003q3
1999q1
2000q3
1997q3
1996q1
1994q3
1993q1
-6 -4 -2
0
2
4
Net Capital Flows (in % of GDP)
ElSalvador
-10
-30
-20
0
10
Net Capital Flows (in % of GDP)
Ecuador
Time
(e)
(f)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
-2 -1
0
1
2
3
Net Capital Flows (in % of GDP)
Mexico
-4
-2
0
2
4
Net Capital Flows (in % of GDP)
Guatemala
Time
Time
(g)
(h)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
50
Figure 1.7: Surge and Flight Episodes: Latin and Central American (Cont.)
2010q1
2011q1
2012q1
2013q1
2014q1
2009q1
2010q3
2012q1
2013q3
2009q1
2008q1
2007q3
Time
2007q1
2006q1
2005q1
2004q1
2003q1
2002q1
2001q1
2013q3
2012q1
2010q3
2009q1
2007q3
2006q1
2004q3
2003q1
2001q3
2000q1
1998q3
1997q1
1995q3
1994q1
1992q3
-4
-2
0
2
4
6
Net Capital Flows (in % of GDP)
Paraguay
-10
-5
0
5
10
Net Capital Flows (in % of GDP)
Nicaragua
Time
(i)
(j)
Time
2006q1
2004q3
2003q1
2001q3
2000q1
1998q3
1997q1
1995q3
1994q1
2013q3
2012q1
2010q3
2009q1
2007q3
2006q1
2004q3
2003q1
2001q3
2000q1
1998q3
1997q1
1995q3
1994q1
1992q3
1991q1
-6
-4
-2
0
2
4
Net Capital Flows (in % of GDP)
Venezuela
6
-2
0
2
4
8
Net Capital Flows (in % of GDP)
Peru
Time
(k)
(l)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
51
Figure 1.8: Surge and Flight Episodes: Asia
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
-1
0
1
2
3
Net Capital Flows (in % of GDP)
India
1
-1
-.5
0
.5
Net Capital Flows (in % of GDP)
Bangladesh
Time
Time
(a)
(b)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
2014q1
2011q3
2006q3
2009q1
2001q3
2004q1
1996q3
1999q1
1991q3
1994q1
1989q1
1984q1
1986q3
1981q3
-4
-2
0
2
4
Net Capital Flows (in % of GDP)
Korea
-2
0
-4
2
Net Capital Flows (in % of GDP)
Indonesia
Time
Time
(c)
(d)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
52
Figure 1.8: Surge and Flight Episodes: Asia (Cont.)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
2013q3
2012q3
2011q3
2010q3
2009q3
2008q3
2007q3
2006q3
2005q3
2004q3
2003q3
2002q3
2001q3
2000q3
1999q3
1998q3
-1
0
1
2
Net Capital Flows (in % of GDP)
Pakistan
0
1
2
3
Net Capital Flows (in % of GDP)
Myanmar
Time
Time
(e)
(f)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
-2
-1
0
1
2
3
Net Capital Flows (in % of GDP)
SriLanka
-4
2
4
-2
0
Net Capital Flows (in % of GDP)
Philippines
Time
Time
(g)
(h)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
53
Figure 1.8: Surge and Flight Episodes: Asia (Cont.)
Time
2014q1
2012q3
2011q1
2009q3
2008q1
2006q3
2005q1
2003q3
2000q3
2002q1
1999q1
1997q3
1996q1
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
-2
0
2
4
6
8
Net Capital Flows (in % of GDP)
Vietnam
-5
5
0
Net Capital Flows (in % of GDP)
Thailand
Time
(i)
(j)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three state
Markov-switching model with switches in mean of absolute value of the net capital flows (in percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al. (2014),
i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire sample’s
distribution of net capital flows (in percentage of GDP). All values of net capital flows in percentage
of GDP above the red line qualify as surges as per the threshold criteria.
54
Figure 1.9: Surge and Flight Episodes : Europe and CIS
Time
2014q1
2012q3
2011q1
2009q3
2008q1
2006q3
2005q1
2003q3
2002q1
2000q3
1999q1
1997q3
1996q1
2014q3
2013q1
2011q3
2010q1
2008q3
2007q1
2005q3
2004q1
2002q3
2001q1
1999q3
1998q1
1996q3
1995q1
-2
0
2
4
6
Net Capital Flows (in % of GDP)
Belarus
2
-2
0
4
6
Net Capital Flows (in % of GDP)
Albania
Time
(a)
(b)
1990q1
1991q3
1993q1
1994q3
1996q1
1997q3
1999q1
2000q3
2002q1
2003q3
2005q1
2006q3
2008q1
2009q3
2011q1
2012q3
2014q1
2014q3
2013q1
2011q3
2010q1
2007q1
2008q3
2005q3
2004q1
2002q3
2001q1
1999q3
1998q1
1995q1
1996q3
-5
0
5
10
Net Capital Flows (in % of GDP)
Hungary
8
-2
0
2
4
6
Net Capital Flows (in % of GDP)
CzechRepublic
Time
Time
(c)
(d)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
55
Figure 1.9: Surge and Flight Episodes: Europe and CIS (Contd.)
2014q1
2011q1
2012q3
2012q3
2009q3
2006q3
2008q1
2005q1
2011q1
Time
2003q3
2002q1
1999q1
2000q3
1997q3
1996q1
1994q3
2014q1
2012q3
2009q3
2011q1
2006q3
2008q1
2005q1
2003q3
2002q1
2000q3
1999q1
1996q1
1997q3
1994q3
1993q1
-5
0
5
10
Net Capital Flows (in % of GDP)
Lithuania
-10
-5
0
5
10
Net Capital Flows (in % of GDP)
Latvia
Time
(e)
(f)
Time
2014q1
2009q3
2008q1
2006q3
2005q1
2003q3
2002q1
2000q3
1999q1
1997q3
1996q1
1994q3
1993q1
1991q3
2013q3
2011q3
2009q3
2007q3
2005q3
2003q3
2001q3
1999q3
1997q3
1995q3
1993q3
1991q3
1989q3
1987q3
1985q3
-2
0
2
4
6
Net Capital Flows (in % of GDP)
Romania
-5
5
-10
0
Net Capital Flows (in % of GDP)
Poland
Time
(g)
(h)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
56
Figure 1.9: Surge and Flight Episodes: Europe and CIS (Contd.)
Time
2014q1
2012q1
2008q1
2010q1
2006q1
2002q1
2004q1
2000q1
1998q1
1996q1
1994q1
1992q1
1990q1
1988q1
1986q1
1984q1
2014q3
2013q1
2011q3
2010q1
2007q1
2008q3
2004q1
2005q3
2002q3
2001q1
1999q3
1998q1
1996q3
1993q3
1995q1
-4
-2
0
2
4
Net Capital Flows (in % of GDP)
Turkey
-10
0
5
-5
Net Capital Flows (in % of GDP)
Russia
Time
(i)
(j)
2014q3
2013q1
2011q3
2010q1
2008q3
2007q1
2005q3
2004q1
2002q3
2001q1
1999q3
1998q1
1996q3
1995q1
1993q3
-5
0
5
10
Net Capital Flows (in % of GDP)
Ukraine
Time
(k)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
57
3
2
1
0
2014q3
2013q3
2012q3
2011q3
2010q3
2009q3
2008q3
2007q3
2006q3
2005q3
Time
(b)
-2
-1
0
1
2
3
SouthAfrica
Net Capital Flows (in % of GDP)
(a)
1980q1
1982q1
1984q1
1986q1
1988q1
1990q1
1992q1
1994q1
1996q1
1998q1
2000q1
2002q1
2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
2004q3
-1
Time
2003q3
2002q3
4
2
0
-2
-4
1980q1
1982q1
1984q1
1986q1
1988q1
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1994q1
1996q1
1998q1
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2004q1
2006q1
2008q1
2010q1
2012q1
2014q1
Morocco
Net Capital Flows (in % of GDP)
Israel
Net Capital Flows (in % of GDP)
Figure 1.10: Surge and Flight Episodes: Other
Time
(c)
Notes: Different sub-figures represent different countries. Right axis of each graph plots the net
capital flows in percentage of GDP. Gray shaded bars indicate periods of surges (extreme inflows)
and green shaded bars indicate periods of flights (extreme outflows) as identified by the three
state Markov-switching model with switches in mean of absolute value of the net capital flows (in
percentage of GDP). Red line is the effective threshold criteria for surges following Ghosh et al.
(2014), i.e., either top 30th percentile of the country’s distribution or top 30th percentile of entire
sample’s distribution of net capital flows (in percentage of GDP). All values of net capital flows in
percentage of GDP above the red line qualify as surges as per the threshold criteria.
58
Chapter 2
Effects of Extreme Capital Flows
on Emerging Markets’ Outcomes:
Is it a Blessing or a Curse?
2.1
Introduction
Following the Great Recession of 2008-09, there has been a renewed interest in understanding cross border capital flows into and out of emerging markets, which experienced exceptionally large inflows in capital when the interest rates in the developed
nations hit the “zero lower bound” (See Fratzscher, Lo Duca, and Straub, 2013;
Binici and Yrkoglu, 2011; Balakrishnan et al., 2013). This led to a concern among
policymakers in the emerging markets that once the developed nations start unwinding such unconventional expansionary monetary policies, there might be a reversal
in the direction of the capital flows from these emerging markets, which may have
negative consequences on the emerging economies (see Eichengreen and Gupta, 2015;
59
Aizenman, Binici, and Hutchison, 2014; Powell, 2013, among others).
A substantial literature focuses on the determinants of the extreme capital flow
episodes. Forbes and Warnock (2012) analyze determinants of extreme movements in
both inflows and outflows using gross flow data for a sample of both developed and
emerging markets. Ghosh et al. (2014) analyze the determinants of surges (abnormally
large net inflows) specifically for emerging market economies, while Ahmed and Zlate
(2013) analyze the determinants of net capital inflows in general, and not just the
surges in net private flows. These recent studies identify various global factors (“push
factors”) and domestic factors (“pull factors”) as important determinants of capital
flow episodes.1
Analysis of impacts of extreme flows on emerging economies’ outcomes have been
less explored. The existing literature analyzes their impacts on various macroeconomic and financial variables like on output growth, real exchange rate, current account, credit growth as well as on the likelihood of financial crises (e.g., Caballero,
2016, Hutchison and Noy, 2006, Edwards, 2000, Cardarelli, Elekdag, and Kose, 2010).
I focus on the potential effects of the extreme movements in net capital flows
on GDP growth for emerging markets. Whether the impact of these extreme flows
(inflows or outflows) is a blessing or a curse for the emerging economy is still an open
debate. According to the IMF Executive Summary Report (2012), capital flows can
be beneficial as it allows for efficient allocation of resources, but at the same time
it can also amplify financial and macroeconomic volatility through appreciation or
depreciation of domestic currency. Understanding these impacts is crucial for making
policy choices in the emerging market economies. The IMF now acknowledges that
1
Other related studies include for example, Fernandez-Arias (1996), Chuhan, Claessens, and
Mamingi (1998), Kim (2000), Fratzscher (2011)
60
regulating capital can be necessary under certain circumstances.
Standard textbook models of the open economy, such as the Mundell-Fleming
model, suggest that surges in capital flows cause appreciation of real exchange rates,
leading to a decline in net exports (see Blanchard et al., 2015a).2 This in turn
leads to a decline in the output of the country for a given domestic monetary policy
rate. Blanchard et al. (2015a) extend these standard textbook models to incorporate
different kinds of flows, in particular, bond and non-bond flows, and analyze the
effects of capital inflows on emerging markets’ output and credit growth rates. They
conclude that the effect of capital inflows on output depends on the nature of the
flows. Blanchard et al. (2015a) did not consider any extreme movement in capital
flows like surges or flights.
Reinhart and Reinhart (2009), identify abnormal increase in capital inflows (“bonanzas”), using current account data as proxy, and analyze their impacts. They find
that capital flow bonanzas are associated with high output volatility as well as lower
output growth in the years following bonanzas. However, they do not conduct any
causal analysis. Also their sample of countries is not restricted to emerging markets.
Powell and Tavella (2015) analyze the impact of surges in gross capital inflows on the
likelihood of recession and banking crisis for a sample of 44 emerging countries .
I shed further light on this strand of the literature by analyzing the impact of
extreme events in both capital inflows and outflows for emerging markets. One of the
concerns with the earlier studies is that there is no consensus in defining these extreme
capital flows. For example, Reinhart and Reinhart (2009) define capital flow bonanzas
for a country as periods where the capital flows are in the top 20 percentile of their
country-specific distribution. Powell and Tavella (2015) define surges as deviation
2
See (Mundell, 1963; Fleming, 1962; Dornbusch, 1976).
61
from their historical trend and also define a threshold value for the deviation.3 I
use a formal statistical model to identify the surges and flights. The first chapter of
my dissertation, proposes a statistical model for identifying these extreme flows. In
particular, I use a three state Markov Switching model to identify periods of extreme
flows (surges and flights), periods of high flows, and period of low flows by allowing
the means of the absolute values of the flows to switch between the states. I provide
a brief description of the method of identification of the surges and flights using the
Markov switching model in Section 2.2.
There is a potential “selection on observables” problem while analyzing effects
of surge and/or flights on country’s output using observational data. The literature
on the causes of these extreme capital flows suggests that there are some global and
domestic factors that are important influences on the incidence of these episodes.
For example, countries with a higher growth rate of output and more open capital
accounts tend to have a higher incidence of surges (Ghosh et al. (2014)). This implies
that occurrence of these extreme flows are not the result of a random experiment. In
such circumstances, standard OLS estimation is likely to give biased results. I address
this issue by using “Inverse Probability Weighting” estimator as in Angrist, Jordà,
and Kuersteiner (2013). It is a method widely used in the microeconomic studies to
take into account the non random assignment of a treatment variable. The method
involves weighting each observation by the inverse probabilities or propensity scores
of the endogenous treatment variables then using these weights in calculating mean
of the outcome variable in the treated and the non-treated sample. The endogenous
treatments here are the occurrence of surges and flights. The propensity scores are
the estimated probabilities obtained from fitting a probit or a logistic model of the
3
They define a surge if the gross inflow is one standard deviation point higher than its historical
trend.
62
endogenous treatment variable on various indicators that might plausibly influence
receipt of the treatment. This inverse weighting assigns a higher weight to the observations that received a treatment but were less likely to be treated. Similarly, for
the observations that did not receive a treatment but were more likely to receive one
are assigned a higher weight. Using this propensity score method in a time -series
framework allows a causal analysis of the extreme capital flows on countries’s output.
In particular, I conduct a dynamic analysis of the effects of the surges and flights
separately on countries’s GDP growth using the local projection inverse probability weighting regression adjustment estimator proposed by Jordà and Taylor (2013).
This methodology uses the inverse probability weighting estimator in a local projection framework, where the local projection method as proposed by Jordà (2005) is
an alternative to the standard vector autoregression (VAR) approach for analyzing
dynamic impulse responses. Local projections are based on dynamic multi-step forecasting. It is a more flexible method than the VAR approach. It allows for inclusion
of other variables and lags in the specification of the conditional mean of the outcome
variable. Thus this framework allows to incorporate the inverse probability weighting
estimation to take care of the selection bias and obtain dynamic impulse responses of
surges and flights on various economic outcomes.
The results indicate that there is detrimental effect on emerging market’s output
in the medium term following a surge, but there is no significant effect on output in
the year immediately after the surge. Five years after a surge, the level of output is
3.6 percentage points lower than it would have been had the surge not taken place.
Also, the effect is found to be underestimated when surges are defined using Reinhart
and Reinhart (2009) methodology.
For flights, the OLS estimates indicate there is a contractionary impact on output
63
in the same year to five years out following the incidence of a flight. The magnitude of
the effect grows with the horizon. The real GDP growth rate for a country receiving
a flight is 1.6 percentage points lower than if it had not received a flight whereas five
years later the level of output is 2.7 percentage points lower. However, the estimation
based on propensity score methods suggests that there is no significant effect of flight
on output. The results are not statistically significant. But the coefficients are all
negative. This is in contrast to the earlier studies which usually associate flights with
contraction in output.
The rest of the paper is organized as follows. Section 2.2, presents a brief description of the method to identify the surges and flights. Section 2.3 presents the
empirical methodology for analyzing the causal effects of the extreme flows. The
results are discussed in the Section 2.4 and, finally, Section 2.6 concludes.
2.2
Extreme Flows: Identification
The first chapter of my dissertation focuses on the identification of extreme events
in net capital flows as a percentage of GDP, both into and out of the country, using
a sample of 36 emerging market economies for the period 1980 to 2014.4 The list of
countries are provided in the Appendix B.1. The data for net private capital flows
is obtained from the IMF Balance of Payments Statistics database (IMF-BOP). The
series is constructed by using net financial account data from the IMF BOP database
and subtracting official government loans which are essentially not part of private
capital flows.
4
The data period ranges from 1980 to 2014 for most of the countries in the sample. The starting
and end period of the data, however, varies with countries.
64
The surges and flights in net capital flows are identified using a three state Markov
switching model allowing switches in the mean of the absolute values of the net flows
for each country where the three states are extreme, high, and low net flows. In
particular, surges are defined as the observations in a country where the probability
of being in the extreme state is higher than the high and the low states, and have
positive net flows. Similarly, the flights are defined as the observations in a country
where the probability of being in the extreme state is higher than the high and the
low states, and have negative flows on a net basis. I also define the high and low net
inflows and outflows in a similar way.
In this paper, I focus only on the extreme events and use the periods of surges
and flights identified in my first chapter to analyze their impacts on the country’s
economies. I found, out of the total sample of 36 countries over the period of 35
years from 1980 to 2014, 7.8% of country-year observations were surges and 3% of
country-year observations were flights.The mean value of net flows in surges is 3.2%
of GDP, whereas for flights is -3.3% of GDP.
These surges and flights are identified using quarterly data on net capital flows
as a percentage of GDP. However, the output data for these countries are available
only at an annual level for the entire sample period. Hence, I convert these quarterly
surges and flights to an annual frequency for each country. I consider a year to be in
a surge if there is at least one quarter in surge in that year. Similarly, if there is a
flight in at least one quarter in a year in a country, the entire year is considered to be
in a flight for that country. A summary of the annual surges and flights by country
is provided in Table 2.1. There are total of 171 surges (18%) and 73 flights (8%) out
of the total sample.
65
2.3
Methodology
My objective is to look at the impact of the extreme episodes in net capital flows
(both inflows and outflows) on emerging market economies. Specifically, I conduct a
dynamic analysis of the effects of the extreme flows on country’s real GDP growth. In
order to do that, I employ the local projection (LP) framework as proposed by Jordà
(2005), which is an alternative method of looking at the impulse responses of a shock
in some economic variable of interest. This method provides a more flexible framework
for modeling the dynamic responses in comparison to the standard VAR approach
of estimating impulse response functions. It is easier to use in a panel framework,
allows estimation of impulse responses for non-linear models. It also allows to include
other variables in the specification. For detailed explanation of the framework, please
refer to Jordà (2005). I provide a brief description of the framework in the following
section.
2.3.1
Local Projection Framework
The LP method calculates the h-step ahead linear projection of the response of an
outcome variable yit for country i in period t to a treatment variable, say Dit , by
estimating the following regression :
ex
0
yi,t+h − yi,t = αh,ex + β h,ex Di,t+1
+ Xi,t
Ωh,ex + αiex + ex
i,t+h ,
(2.1)
for h = 1, 2, 3, 4, 5, ..., T . The term yi,t+h − yi,t denotes the log change in the outcome
variable between period t and t + h in country i. I consider the real GDP growth rate,
current account balance (as percentage of GDP), real and nominal exchange rate as
66
ex
outcome variables. Di,t
is a binary variable which takes value 1 if there is an extreme
episode, and 0 otherwise in country i in period t. “ex” takes on values s for surges,
and f for flights. X is a vector of control variables, for example lagged values of the
outcome variables. I estimate the above equations separately for surges and flights
using the definition of surges and flights as described in Section 2.2. The coefficient
β h,ex captures the cumulative effect over h periods of an extreme flow on the outcome
variable.
The occurrence of these extreme events in capital flows is not a result of a random
experiment. It suffers from the “selection-on-observable” problem. There may be
some observable components that may cause these extreme movements in net capital
flows in different countries in different years. In such circumstances, it is hard to
disentangle the effect of a treatment on the outcome variable. Thus, OLS estimation
results are likely to be biased. In order to address this issue, I use the Inverse Probability Weighted Regression Adjusted Estimator (henceforth, IPWRA) as described in
Jordà and Taylor (2013). This estimator combines the Inverse Probability Weighting
Estimator of Angrist, Jordà, and Kuersteiner (2013) with the regression adjustment
and the local projection method of Jordà (2005), so as to orthogonalize the treatment
with respect to potential outcomes and estimate the dynamic impulse responses.
2.3.2
Inverse Probability Weighting Estimator
This method uses a two step procedure to address the selection-on-observable problem
in the data. This is fairly common in the empirical microeconomic research arena.
However, application to macro data is relatively new. In the first step, I run a
regression of the treatment variable on various indicators that can be thought of as
67
explaining it. I resort to the economic literature to select these explanatory variables.
Using the inverse of the fitted value of this regression, I weight each observations in
the data. Thus, the observations which were less likely to be treated but actually
did receive the treatment get more weight relative to the observations which received
the treatment when they were more likely to receive it. The same is true for the
untreated observations.
In my analysis, the occurrence of the extreme flow, whether inflow or an outflow,
can be thought of as the treatment. There is a vast literature that studies the determinants of these extreme inflows. A recent study by Ghosh et al. (2014) find that
there are certain global factors (“push factors”) as well as certain domestic factors
(“pull factors”) that explain extreme inflows. The push factors are global factors
whereas the pull factors are the domestic factors for the net capital flows. The global
factors that they find significant include the real interest rate for the US, change in
the world commodity price, and global market volatility measured by the volatility in
the S&P 500 price index.5 Among the domestic pull factors, they find that a country’s
growth rate of real GDP and capital account openness as significant determinants. I
compare the mean values of these macroeconomic indicators across the treated and
the untreated groups.
The domestic macroeconomic variables that I consider are domestic real GDP
growth rate and index for capital account openness. I use the capital account openness index from Chinn and Ito (2008) as a measure of the degree of financial openness. A higher value of the index implies country is more open. The means of these
macroeconomic variables across surge and no-surge observations, and flight and noflight observations are reported in Table 2.2. The upper panel of the table gives the
5
They use the Chicago Board Options Exchange (CBOE) data on VIX to measure the volatility
in the S&P 500 index.
68
means across surge and no-surge observations and the lower panel gives the means
across flight and no-flight observations. The third row of each panel in Table 2.2 gives
the difference between the estimated mean values of the different sub populations for
surges and flights.
The upper panel of Table 2.2, shows that the surge and the no-surge observations
have significantly different growth rates of real GDP before the surge. The countryyear observations receiving surges are associated with a higher real GDP growth rate
on average. Also, these periods are associated with a significantly lower mean real
interest rates in the US and a higher value of commodity price growth on average.
I find that the observations that are receiving extreme inflows on a net basis are
more open on average than the no-surge observations. However, this difference is not
statistically significant. The global market volatility is lower on average for the surge
observations than the no-surge observations. Again, this difference is not statistically
significant.
Turning our focus towards flights, we find the country-year observations experiencing extreme outflows, are associated with significantly lower growth rate in real
GDP than those not experiencing any flights. Also they correspond to periods of
significantly higher global market volatility than the no-flight observations.
Given that the means of some of the variables are significantly different between
the sub-populations receiving extreme flows and the ones not receiving extreme flows,
this implies that the treatment (extreme inflows) are not exogenous. There is likely
to be some allocation bias. There are several ways to deal with endogeneity problems in econometrics. The most common solution is to use an instrumental variable
approach. However, for this method we need some variable that is highly correlated
with the extreme flows indicator variable but not correlated with the error term i.e,
69
the error term satisfy the exclusion restriction. It is difficult to find such pure instruments. In the absence of proper instruments, the approach that can be used is the
propensity score weighting approach. The objective of this method is to match the
two sub populations by assigning different weights to them. The estimator proposed
by Angrist, Jordà, and Kuersteiner (2013), henceforth AJK, uses this approach and
applies it in a time-series environment.
The AJK-estimator involves a two step procedure. The first step involves running
a regression of the treatment variable on the various explanatory variables to get the
propensity score of the treatment variable (Di,t ). In particular, I run a probit model
to get these propensity scores using the fitted value of the regression as described
below:
∗
Di,t
= µ + λXi,t + νit ,
(2.2)
where Dit∗ is the underlying continuous latent variable for the observed treatment
variable, Di,t that can be interpreted as the likelihood of the occurrence of extreme
flows. The observed variable, Di,t is a realization of an extreme flow when the latent
variable is positive:
Di,t =


 1 D∗ > 0
i,t

 0 otherwise.
Xit is the vector of explanatory variables for the treatment. The fitted value of
Equation (2.2) gives the propensity scores for the treatment variable, Dit which are
used for weighting each observation in the sample. Let P (Dit = dj |Xit ) = pj (Xit ) for
j = 1, 0, denote the propensity score for country i in period t with a treatment j,
70
where p1 (Xit ) = 1 − p0 (Xit ). The AJK estimator is given by:6
T
T
1 X (yi,t+h − yi,t )1(Di,t = 0)
1 X (yi,t+h − yi,t )1(Di,t = 1)
−
θ̂ =
T t=h+1
p̂1 (Xi,t )
T t=h+1
p̂0 (Xi,t )
h
(2.3)
for h=0, ....., H. yi,t+h − yi,t is the difference in the values of outcome variable between
period t and t+h. 1(.) is an indicator function for the treatment variable, Di,t , p̂1 (Xi,t )
and p̂0 (Xi,t ) are the estimated probabilities of treatment, Di,t , from the probit model
described in Equation (2.2).
The term
1
T
PT
t=h+1
(yi,t+h −yi,t )1(Di,t =1)
p̂1 (Xi,t )
in Expression (2.3) gives the average effect
on the outcome variable h periods after the intervention of the treatment which takes
place in period t weighted by the inverse of the estimated probability of treatment conP
(y
−yi,t )1(Di,t =0)
ditional on country i receiving treatment in period t. The term T1 Tt=h+1 i,t+h p̂0 (X
i,t )
gives the average effect on the outcome variable h periods after the intervention of
the treatment which takes place in period t weighted by the inverse of the estimated
probability of treatment conditional on country i not receiving any treatment in period t. Thus, the method uses the estimated inverse probability of the treatment to
weight each observation (country-year) in the data when calculating the mean of the
outcome variable of the treated and the non-treated sample. The estimator gives the
difference in the average outcome of the treated sample and the non-treated sample
using the weighted observations.
Jordà and Taylor (2013) use the Inverse Probability Weighted Regression Adjusted estimator in a Local Projection framework. The regression adjustment method
models the outcome variable to take into account the nonrandom assignment of the
6
j = 1 indicates a treatment.
71
treatment whereas IPW estimation models the treatment variable. The IPWRA estimator combines the two methods by using the IPW weights to estimate the corrected
regression coefficients that are used in the regression adjustment estimation. It belongs to the class of “doubly robust” estimators, which means the estimate of the
treatment effect is consistent if either the treatment model or the outcome model
(not both) are mis-specified.
2.4
Results
In this section, I present the results of the LP-IPWRA estimation to find the effect
of extreme net flows, both into and out of the country, using the estimation methodology mentioned in Section 2.3. I conduct the dynamic analysis of surges and flights
separately. I first discuss the results of the baseline regression using the LP-OLS
estimation as described in Expression 2.1.
2.4.1
Effect of Surges
2.4.1.1
LP-OLS Estimation
Results of the estimation of Expression 2.1 for surges on real GDP growth are presented in Table 2.3. The local projection for output is done for up to five years out
after there is an extreme inflow in a country. Table 2.3 gives the estimation results
without any country fixed effects on the upper panel. I also estimate the equation
by including country fixed effects, the result of which are shown in bottom panel of
Table 2.3.
Table 2.3 shows that the real GDP growth rate is 0.3 percentage points higher in
72
the year when the country experiences a surge than the countries that do not experience a surge (the control group). But from the second period onwards the growth
rate in the countries experiencing surges is lower than in the control group. Also,
the magnitude of the effect goes up significantly. In two periods after an incidence
of surge in country i, the real GDP growth is 0.02 percentage points lower than in
the control group. However, after four periods the growth rate of real GDP is 2.7
percentage points lower, and after 5 years, the real GDP is 4 percentage points lower
than the observations that did not receive any surge in period t. The effect of surges
on real GDP is not statistically significant in the first 3 years, but becomes so from
the fourth year onwards.
With the inclusion of country fixed effects in the regression, the initial effect of
the surges on the real GDP growth rate becomes statistically significant and also the
magnitude is a little higher. The difference between the observations receiving a surge
and the ones not receiving a surge is 0.8 percentage points. But the magnitude of the
cumulative effect of surges on the growth rate in the medium term is lower compared
to the results without fixed effects, though it is still statistically significant after 5
years.
Table 2.4 presents results of the OLS estimate of effect of surges on country’s
current account balance in percentage of GDP in a local projection framework. Again,
the OLS estimates without fixed effects are shown in upper panel and the estimates
with country fixed effects are shown in the bottom panel of the table. The surges
appear to have a negative effect on the current account balance as indicated by
the negative coefficients of the estimates in first row of the Table 2.4. The effect is
stronger in the initial years after the occurrence of surge. The current account balance
as percentage of GDP is 1.5 percentage points lower in the year of the occurrence of
73
surges than had there been no surge. However, five year after the occurrence of a
surge, it is 0.7 percentage of GDP higher than had there not been any surge.
The OLS estimates of the effect of surge on real exchange rate is shown in the Table
2.5. The real exchange rate are measured as the value of domestic goods in terms of
foreign goods. So an increase in the real exchange rate implies an appreciation and
vice versa. The estimated coefficients from the upper panel of the table show that the
real exchange rate appreciates following an occurrence of a surge. However, the OLS
estimates for the nominal exchange rates shows that there is a nominal depreciation
of the currency (see first row of the Table 2.6).
As discussed in the Section 2.3, the LP-OLS estimate suffers from endogeneity
issue given the non random assignment of the extreme flows across country and time.
In the next section, I discuss the estimation results using the LP-IPWRA estimation.
2.4.1.2
LP-IPWRA Estimation
This section presents the result of the IPWRA estimation of average treatment effects
of surges on country’s output growth rate, current account balance, real exchange
rate, and nominal exchange rate. The explanatory variables for the probit analysis,
as described in the Expression 2.2, are obtained from the existing literature that
analyzes the determinants of surges. In particular, I include real the interest rate
in the US, a measure of global financial market uncertainty (the volatility in S&P
500 index), growth of world commodity prices, lagged growth rate of domestic real
GDP, and index of capital account openness (the Chinn-Ito index). For the outcome
model, I include a lagged value of the log change in real GDP to account for the serial
correlation in the growth rate of output.
74
Table 2.7 presents the LP-IPWRA estimates for average treatment effects of the
occurrence of surges on a country’s output in the first row. The results indicate that
even after taking into account the non random assignment of the occurrence of surges,
there is a detrimental effect on the country’s output growth rate in the medium term.
The results are similar to the OLS results reported in Table 2.3. The evidence is
stronger with the IPWRA estimation. The estimate of the accumulated effect after
two years is now significant at the ten percent level, and the estimates after three
years onwards are significant at the one percent level.
The results of the probit model are also shown in the bottom part of the Table. All
the variables except the index for capital account openness are statistically significant
and the coefficients have the expected signs. A higher real interest rate in the US, and
a higher volatility in global markets as measured by the volatility in the S&P 500 index
causes a lower probability of surges in emerging markets. There is a higher likelihood
of extreme inflows in capital in emerging markets when the country’s growth rate is
higher, and also when the growth in world commodity prices are higher.
The effect on current account balance is negative. Table 2.8 reports the average
treatment effects estimation results for surges on current account balance. However,
compared to the OLS result the effect is weaker. The current account deficit as
percentage of GDP for the observations receiving surge is now 0.6 percentage points
higher than the observations not receiving any surge in the same year of occurrence
of a surge.
The average treatment effect of a surge on real and nominal exchange rate using
LP-IPWRA estimation are reported in tables, 2.9 and 2.10. After taking into account the allocation bias in the surge occurrence, there is no effect of surge on the
real exchange rate. However, we encounter a significant appreciation of the nominal
75
exchange rates after occurrence of a surge (See columns . The estimates are all significant at one percent level of significance. This is in contrast to the OLS results
where the estimates for the real exchange rate were significant.
2.4.2
Effect of Flights
This section presents the effects of an extreme outflow the same macroeconomic variables as discussed in previous sections, output growth, current account balance, real
and nominal exchange rates. Again, I present the results for the LP-OLS specification
first in Section 2.4.2.1 and then discuss the LP-IPWRA estimates in Section 2.4.2.2
2.4.2.1
LP-OLS Estimation
Table 2.11 reports the results of estimation of effects of flight on output growth using
the LP-OLS estimation. The upper panel presents the OLS results without any fixed
effects while the bottom panel shows the result of OLS estimation with country fixed
effects. Similar to the surge analysis, I also estimate Expression 2.1 for flights by
incorporating country fixed effects. The results with fixed effects are presented in
Table
The results indicate that there is a detrimental effect on a country’s output when
the country experiences an extreme outflow of capital on a net basis. Table 2.11
shows that the real GDP growth rate is 1.6 percentage points lower than in the
control group in the same year when there is a flight and it continues to be lower by
a larger magnitude in the second and third year. The estimates are highly significant
for the first three years after an occurrence of a flight. Even after controlling for
the country specific time-invariant unobserved differences, the results are similar as
76
evident from the results reported in the bottom panel of Table 2.11.
The average treatment effect of flight on current account balance is positive as
indicated by the results in Table 2.12. The effect is statistically significant for the
first three years after the occurrence of a flight. Tables 2.13 and 2.14 report the OLS
estimation results for real and nominal exchange rates. The flight are associated an
immediate depreciation in both real and nominal exchange rates.
2.4.2.2
LP-IPWRA Estimation
The LP-IPWRA estimates for the average treatment effect of flights of capital on its
output growth rate are reported in Table 2.15 in the first row. The results indicate
that after taking into account the non random assignment of the treatment, the
effect of an extreme outflow of capital on the country’s output growth is no longer
statistically significant. This is an interesting result as capital flights have often been
thought of as having a detrimental effect on country’s growth rate.
Table 2.16 reports the average treatment effect of flight on current account deficits.
The results show that the flights cause the current account deficits to significantly
increase in the medium and long term (see Columns (3)-(4)). This is opposite to
what the OLS results indicate.
The LP-IPWRA estimates for the real exchange rate as shown in Table 2.17 suggest that the flights cause the real exchange rate to depreciate immediately after a
flight (Column 1) but in the long run there is an real appreciation of the exchange
rate. However, the estimates are not statistically significant except for the last year
(Column (5)). For the nominal exchange rate, however there is a significant appreciation following an occurrence of flight. This is a puzzling result, as flights which are
77
abnormally high level of capital outflows from a country is usually expected to cause
a downward pressure on the value of the currency. But it should be noted that the
regression specification I consider here is a baseline one. Still there is a lot to explore
to find the causality of flights on country’s macroeconomic and financial indicators.
2.5
Comparison with Existing Methodology
In this section, I compare the estimation results for surges on countries’ output using
definition of surges as identified by Reinhart and Reinhart (2009), henceforth, RR
surges. According to their definition, surges are periods where the capital flows are in
the top 20 percentile of their country-specific distribution. Given that the data and
sample of countries used in by Reinhart and Reinhart (2009), are different from the
sample I consider here, I apply their definition on my sample and identify surges.7
Applying their definition to my sample, I find there are 446 periods of surges across
countries and year.
Table 2.19 presents the OLS estimation result of an effect of surges on countries’
output using the two definitions of surges. The top panel reports the results using
definition of surges based on Markov switching model, henceforth, MS surges, and
the bottom panel reports the results for RR surges. The Table shows that the RR
surges have a significant positive effect on the real GDP growth rate in the first two
years after a surge. The effect is negative after five years of occurrence of a surge,
however, the estimate is not statistically significant. This is in contrast to the results
encountered for the MS surges. Allowing for country fixed effects, the effect of RR
7
Since, the surges using Markov switching model were based on quarterly data of surges, and
then converted to an annual frequency (see Section 2.2), I also do the same for the methodology
for RR. I identify the periods of surges based on quarterly data and then convert them into annual
frequency in the similar way I do for the surges identified by Markov switching model.
78
surges is negative and significant on output growth rate in the long term (fourth year
onwards) as indicated by the figures in the bottom panel of the Table 2.20. However,
the effect is weaker in comparison with the MS surges’ results. For the MS surges,
there is a significant negative effect from the second year onwards after a surge, and
the estimates are higher level of significance.
Next, I compare the results of the IPWRA estimates using the two definitions
of surges. Table 2.21 presents the results of the IPWRA estimation. The top panel
presents the results for MS surges and the bottom panel for RR surges. Again, we
encounter that the RR surges have a weaker effect on real GDP growth rate than the
surges identified using MS model. The point estimates for the RR surges are smaller
than the MS surges for the last three years. Five years out after a surge, the output
is 3.6 percentage points lower than if there were no surge for MS surges, however, for
RR surges, output is 2.7 percentage points lower.
In sum, the results suggest that the surges have a contractionary effect on output.
The RR surges have a weaker effect on output than the MS surges.
2.6
Conclusion
Effect of extreme movements in capital flows on output in emerging market economies
is still an unresolved debate. I revisit this question, by analyzing the effect of surges
(extreme net inflows) and flights (extreme net outflows) on output for a sample 36
emerging market economies using a novel definition for these extreme flows and also a
novel estimation method for conducting causal analysis. In particular, I use definitions
of surges and flights based on a statistical model and propensity score method of
estimation in a time series setting to deal with potential selection-on-observables
79
problem in observational data.
The evidence presented in this paper, suggests that surges in net capital flows have
a contractionary effect on countries’’ output in the medium term. Effects of surges
where surges are identified using threshold methodology used in the literature are
relatively underestimated than the surges defined using a statistical model. However,
for flights there is no robust evidence for having any statistically signficant effect on
output, though the point estimates are found to be negative implying a contractionary
effect on output.
The findings in this paper contribute to the debate of costs and benfits of imposing capital controls for emerging markets. The IMF now acknowledges that under
certain conditions capital controls may be desirable for emrging marktes (see (IMF
Executive Summary Report, 2012)). When a country faces an exceptionally high
levels of capital inflows, imposing some restrictions on the cross-border captal inflows
may be beneficial for country’s growth in the future.
80
Table 2.1: Annual Surges and Flights by Country
country
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
Total
N
nci lgdp pc
20
35
34
19
35
24
19
20
22
16
35
26
35
34
35
35
21
19
35
11
17
23
35
13
23
35
30
24
21
35
35
35
31
21
20
19
947
Sum
Surge countryyear
11
1
5
10
1
7
2
1
0
3
6
12
2
0
9
0
4
4
7
5
1
10
5
3
5
4
0
3
0
11
19
2
9
5
2
2
171
Flight countryyear
0
4
12
0
0
2
0
0
1
3
1
2
0
5
5
2
2
0
2
0
0
2
2
1
0
1
1
0
2
2
3
3
2
3
10
0
73
Notes : The Table reports the total number of surges and flights for the different countries in the
sample at an annual level.
81
Table 2.2: Mean comparison between Sub-Populations with Extreme and No
Extreme Net Capital Flows
Surge:
Variables
Real GDP
Growth Rate
Capital
Account
Openness
Index
Real US
Interest
Rate
World
Commodity
Price Index
Growth
S&P 500
Index
Volatility
Mean (Surge)
Mean (No Surge)
Difference
p-value
4.737
3.709
1.028**
0.003
0.463
0.427
0.036
0.213
0.325
0.815
-0.490**
0.006
0.040
-0.013
0.053***
0.000
20.348
20.969
-0.621
0.332
*** p<0.01, ** p<0.05, * p<0.1
Flight:
Variables
Real GDP
Growth
Rate
Capital
Account
Openness
Real US
Interest
Rate
World
Commodity
Price Index
Growth
S&P 500
Index
Volatility
Mean (Flight)
Mean (No Flight)
Difference
p-value
2.146
4.046
-1.899***
0.000
0.408
0.436
-0.027
0.506
0.674
0.731
-0.056
0.825
-0.022
-0.002
-0.020
0.367
23.039
20.662***
2.378**
0.010
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports means of various macroeconomic variables, real GDP growth rate, capital
account openness index (Chin-Ito Index), US real interest rate (indicator of advanced economy
interest rate), growth in world commodity price index, and global volatility measured by volatility
of S&P 500 index (VIX) for a sample of 36 emerging markets for the period 1980-2014 across surge
and no-surge observations in the upper panel, and across flight and no-flight observations in the
lower panel. The third row in both panel gives the difference between the means of surge and
no-surge observations.
82
Table 2.3: Effect of Surges on Real GDP Growth: LP-OLS estimates
VARIABLES
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
0.314
(0.406)
0.452***
(0.000)
2.005***
(0.000)
-0.023
(0.972)
0.620***
(0.000)
5.456***
(0.000)
-1.232
(0.141)
0.768***
(0.000)
9.191***
(0.000)
-2.663***
(0.008)
0.858***
(0.000)
13.188***
(0.000)
-4.020***
(0.001)
0.878***
(0.000)
17.502***
(0.000)
702
0.188
NO
NO
702
0.128
NO
NO
702
0.115
NO
NO
702
0.103
NO
NO
702
0.088
NO
NO
Surge (t+1)
Real GDP Growth(t)
Constant
Observations
R-squared
Country FE
Year FE
83
VARIABLES
Surge (t+1)
Real GDP Growth (t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-0.248
(0.542)
0.349***
(0.000)
2.533***
(0.000)
-1.303*
(0.050)
0.366***
(0.000)
6.736***
(0.000)
-2.665***
(0.001)
0.367***
(0.000)
11.110***
(0.000)
-3.790***
(0.000)
0.299***
(0.002)
15.713***
(0.000)
-4.226***
(0.000)
0.149
(0.158)
20.568***
(0.000)
702
0.032
36
YES
NO
702
0.024
36
YES
NO
702
0.103
36
YES
NO
702
702
0.047
0.039
36
36
YES
YES
NO
NO
pval in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of log change in real GDP for country i in year t on a binary variable,
s
Di,t
indicating if there is a surge or not, using a local projection framework. Columns (1)-(5) report the local projection for 1 to 5 years out
respectively since the occurrence of a surge for the real GDP growth. Upper panel of the table shows results without any fixed effects, whereas
the bottom panel includes country fixed effects.
Table 2.4: Effect of Surges on Current Account Balance: LP-OLS estimates
VARIABLES
(1)
CA Balance (t+1)
(2)
CA Balance (t+2)
(3)
CA Balance (t+3)
(4)
CA Balance (t+4)
(5)
CA Balance (t+5)
-1.496***
(0.000)
0.760***
(0.000)
-0.189
(0.141)
-1.770***
(0.000)
-0.406***
(0.000)
-0.418***
(0.010)
-1.420***
(0.000)
-0.480***
(0.000)
-0.557***
(0.001)
-0.776*
(0.063)
-0.530***
(0.000)
-0.769***
(0.000)
-0.772*
(0.070)
-0.561***
(0.000)
-0.796***
(0.000)
738
0.646
NO
NO
738
0.223
NO
NO
738
0.263
NO
NO
738
0.286
NO
NO
738
0.301
NO
NO
Surge (t+1)
CA Balance (t)
Constant
Observations
R-squared
Country FE
Year FE
84
VARIABLES
Surge (t+1)
CA Balance (t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
CA Balance (t+1)
(2)
CA Balance (t+2)
(3)
CA Balance (t+3)
(4)
CA Balance (t+4)
(5)
CA Balance (t+5)
-0.825***
(0.009)
0.552***
(0.000)
-0.805***
(0.000)
-0.806**
(0.030)
0.234***
(0.000)
-1.448***
(0.000)
-0.021
(0.955)
0.112***
(0.002)
-1.776***
(0.000)
-0.049
(0.896)
0.002
(0.966)
-2.012***
(0.000)
-0.327
(0.378)
-0.052
(0.158)
-2.044***
(0.000)
738
0.000
36
YES
NO
738
0.003
36
YES
NO
738
0.343
36
YES
NO
738
738
0.074
0.014
36
36
YES
YES
NO
NO
pval in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of current account balance in percentage of GDP for country i in year t on
s
a binary variable, Di,t
indicating if there is a surge or not, using a local projection framework. Columns (1)-(5) report the local projection for 1
to 5 years out respectively since the occurrence of a surge for the current account balance in percentage of GDP. Upper panel of the table shows
results without any fixed effects, whereas the bottom panel includes country fixed effects.
Table 2.5: Effect of Surges on Real Exchange Rate: LP-OLS estimates
VARIABLES
Surge (t+1)
Real Exchange
Rate (t+1)
Constant
Observations
R-squared
Country FE
Year FE
(1)
Real Exchange Rate (t+1)
(2)
Real Exchange Rate (t+2)
(3)
Real Exchange Rate (t+3)
(4)
Real Exchange Rate (t+4)
(5)
Real Exchange Rate (t+5)
-3.354*
(0.063)
-0.098***
(0.010)
-0.060
(0.937)
-5.805**
(0.015)
-0.107**
(0.032)
-0.662
(0.514)
-4.751*
(0.093)
-0.050
(0.401)
-1.473
(0.220)
-4.096
(0.251)
-0.033
(0.662)
-2.468
(0.104)
-4.606
(0.273)
-0.102
(0.244)
-2.908
(0.104)
672
0.014
NO
NO
672
0.015
NO
NO
672
0.005
NO
NO
672
0.002
NO
NO
672
0.004
NO
NO
85
VARIABLES
Surge (t+1)
Real Exchange
Rate (t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
Real Exchange Rate (t+1)
(2)
Real Exchange Rate (t+2)
(3)
Real Exchange Rate (t+3)
(4)
Real Exchange Rate (t+4)
(5)
Real Exchange Rate (t+5)
-1.243
(0.527)
-0.135***
(0.001)
-0.458
(0.558)
-0.065
(0.980)
-0.172***
(0.001)
-1.726*
(0.089)
1.560
(0.601)
-0.139**
(0.018)
-2.647**
(0.026)
2.009
(0.603)
-0.099
(0.191)
-3.596**
(0.020)
2.470
(0.585)
-0.147*
(0.100)
-4.204**
(0.020)
672
0.019
35
YES
NO
672
0.018
35
YES
NO
672
0.009
35
YES
NO
672
0.003
35
YES
NO
672
0.005
35
YES
NO
pval in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of log change in real exchange rate (domestic goods in terms of foreign
s
goods) for country i in year t on a binary variable, Di,t
indicating if there is a surge or not, using a local projection framework. Columns (1)-(5)
report the local projection for 1 to 5 years out respectively since the occurrence of a surge for the log change in real exchange rate.
Table 2.6: Effect of Surges on Nominal Exchange Rate: LP-OLS estimates
(1)
Nominal
Exchange Rate
(t+1)
(2)
Nominal
Exchange Rate
(t+2)
(3)
Nominal
Exchange Rate
(t+3)
(4)
Nominal
Exchange Rate
(t+4)
(5)
Nominal
Exchange Rate
(t+5)
-3.501
(0.272)
0.649***
(0.000)
4.791***
(0.001)
-7.964
(0.164)
1.162***
(0.000)
10.311***
(0.000)
-10.008
(0.218)
1.593***
(0.000)
16.039***
(0.000)
-12.083
(0.241)
1.986***
(0.000)
21.009***
(0.000)
-15.340
(0.214)
2.335***
(0.000)
26.095***
(0.000)
687
0.434
NO
NO
687
0.434
NO
NO
687
0.416
NO
NO
687
0.408
NO
NO
687
0.399
NO
NO
Surge (t+1)
Nominal
Exchange Rate (t)
Constant
Observations
R-squared
Country FE
Year FE
86
Surge (t+1)
Nominal Exchange
Rate (t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
Nominal
Exchange Rate
(t+1)
(2)
Nominal
Exchange Rate
(t+2)
(3)
Nominal
Exchange Rate
(t+3)
(4)
Nominal
Exchange Rate
(t+4)
(5)
Nominal
Exchange Rate
(t+5)
-0.941
(0.784)
0.548***
(0.000)
5.926***
(0.000)
0.628
(0.917)
0.937***
(0.000)
12.334***
(0.000)
2.280
(0.788)
1.234***
(0.000)
19.527***
(0.000)
1.676
(0.875)
1.513***
(0.000)
26.037***
(0.000)
0.799
(0.950)
1.752***
(0.000)
32.426***
(0.000)
687
0.253
35
YES
NO
687
0.244
35
YES
NO
687
0.300
35
YES
NO
687
687
0.287
0.262
35
35
YES
YES
NO
NO
pval in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of log change in nominal exchange rate (domestic currency per foreign
s
currency (US dollars)) for country i in year t on a binary variable, Di,t
indicating if there is a surge or not, using a local projection framework.
Columns (1)-(5) report the local projection for 1 to 5 years out respectively since the occurrence of a surge for the log change in nominal exchange
rate. Upper panel of the table shows results without any fixed effects, whereas the bottom panel includes country fixed effects.
Table 2.7: Effect of Surge on Real GDP Growth: LP-IPWRA estimates
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-0.084
(0.78)
-0.828*
(0.09)
-2.051***
(0.00)
-3.219***
(0.00)
-3.644***
(0.00)
4.225***
(0.00)
8.600***
(0.00)
13.034***
(0.00)
17.386***
(0.00)
21.574***
(0.00)
4.141***
(0.00)
7.772***
(0.00)
10.983***
(0.00)
14.167***
(0.00)
17.931***
(0.00)
-0.067*
(0.09)
-0.067*
(0.09)
-0.067*
(0.09)
-0.067*
(0.09)
-0.067*
(0.09)
-0.013*
(0.09)
-0.013*
(0.09)
-0.013*
(0.09)
-0.013*
(0.09)
-0.013*
(0.09)
World Commodity
Price Growth
1.602***
(0.00)
1.602***
(0.00)
1.602***
(0.00)
1.602***
(0.00)
1.602***
(0.00)
Lag Real GDP Growth
0.053***
(0.00)
0.053***
(0.00)
0.053***
(0.00)
0.053***
(0.00)
0.053***
(0.00)
0.040
(0.34)
0.040
(0.34)
0.040
(0.34)
0.040
(0.34)
0.040
(0.34)
-0.733***
(0.00)
607
-0.733***
(0.00)
607
-0.733***
(0.00)
607
-0.733***
(0.00)
607
-0.733***
(0.00)
607
ATE
Surge (t+1)
Potential Outcome mean
No Surge
Surge
Probit Results
TME1
Real US Interest
Rate
87
S&P Volatility
Capital Account
Openness
Constant
Observations
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of log
s
change in real GDP for country i in year t on a binary variable, Di,t
indicating if there is a surge in capital flows or not, using a local projection
(LP) framework. Columns (1)-(5) report the local projection for 1 to 5 years out respectively since the occurrence of a surge for the real GDP
growth.
Table 2.8: Effect of Surge on Current Account Balance: LP-IPWRA estimates
ATE
Surge
POmean
No Surge
Surge
Observations
(1)
CA Balance (t+1)
(2)
CA Balance (t+2)
(3)
CA Balance (t+3)
(4)
CA Balance (t+4)
(5)
CA Balance (t+5)
-0.636**
(0.03)
-1.024***
(0.00)
-0.426
(0.28)
-0.628*
(0.09)
-0.680
(0.13)
-1.939***
(0.00)
-1.968***
(0.00)
-2.124***
(0.00)
-2.095***
(0.00)
-2.034***
(0.00)
-2.576***
(0.00)
607
-2.992***
(0.00)
607
-2.551***
(0.00)
607
-2.723***
(0.00)
607
-2.714***
(0.00)
607
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
88
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of
s
current account balance in percentage of GDP for country i in year t on a binary variable, Di,t
indicating if there is a surge in capital flows or
not, using a local projection (LP) framework. Columns (1)-(5) report the local projection for 1 to 5 years out respectively since the occurrence
of a surge for the current account balance in percentage of GDP. Upper panel of the table shows results without any fixed effects, whereas the
bottom panel includes country fixed effects.
Table 2.9: Effect of Surge on Real Exchange Rate: LP-IPWRA estimates
ATE
Surge
POmean
No Surge
Surge
[1em] Observations
(1)
Real Exchange Rate (t+1)
(2)
Real Exchange Rate (t+2)
(3)
Real Exchange Rate (t+3)
(4)
Real Exchange Rate (t+4)
(5)
Real Exchange Rate (t+5)
-1.344
(0.35)
0.678
(0.81)
0.637
(0.80)
-1.502
(0.61)
-3.674
(0.26)
-0.836
(0.33)
-2.527**
(0.02)
-4.269***
(0.00)
-4.622**
(0.01)
-4.142*
(0.09)
-2.180*
(0.06)
589
-1.849
(0.49)
589
-3.632
(0.11)
589
-6.124**
(0.01)
589
-7.816***
(0.00)
589
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
89
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of
s
log change in nominal exchange rate (domestic goods in terms of foreign goods) for country i in year t on a binary variable, Di,t
indicating if
there is a surge in capital flows or not, using a local projection (LP) framework. Columns (1)-(5) report the local projection for 1 to 5 years out
respectively since the occurrence of a surge for the log change in real exchange rate.
Table 2.10: Effect of Surge on Nominal Exchange Rate: LP-IPWRA estimates
ATE
Surge
POmean
No Surge
Surge
Observations
(1)
Nominal
Exchange Rate
(t+1)
(2)
Nominal
Exchange Rate
(t+2)
(3)
Nominal
Exchange Rate
(t+3)
(4)
Nominal
Exchange Rate
(t+4)
(5)
Nominal
Exchange Rate
(t+5)
-7.281***
(0.00)
-11.880***
(0.00)
-17.303***
(0.00)
-24.129***
(0.00)
-28.794***
(0.00)
12.194***
(0.00)
22.711***
(0.00)
32.037***
(0.00)
40.985***
(0.00)
49.291***
(0.00)
4.913***
(0.00)
593
10.831***
(0.00)
593
14.734***
(0.00)
593
16.856***
(0.00)
593
20.497***
(0.00)
593
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
90
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of
s
log change in nominal exchange rate (domestic currency per foreign currency (US dollars)) for country i in year t on a binary variable, Di,t
indicating if there is a surge in capital flows or not, using a local projection (LP) framework. Columns (1)-(5) report the local projection for 1
to 5 years out respectively since the occurrence of a surge for the log change in nominal exchange rate.
Table 2.11: Effect of Flight on Real GDP Growth: LP-OLS estimates
VARIABLES
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-1.632***
(0.001)
0.448***
(0.000)
2.225***
(0.000)
-3.274***
(0.000)
0.601***
(0.000)
5.822***
(0.000)
-2.898***
(0.008)
0.734***
(0.000)
9.378***
(0.000)
-2.732**
(0.040)
0.804***
(0.000)
13.193***
(0.000)
-2.724*
(0.075)
0.803***
(0.000)
17.352***
(0.000)
702
0.200
NO
NO
702
0.147
NO
NO
702
0.121
NO
NO
702
0.099
NO
NO
702
0.077
NO
NO
Flight (t+1),
Real GDP Growth(t)
Constant
Observations
R-squared
Country FE
Year FE
91
VARIABLES
Flights (t+1),
Real GDP Growth(t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-1.781***
(0.521)
0.337***
(0.039)
2.700***
(0.222)
-3.499***
(0.849)
0.330***
(0.064)
6.971***
(0.362)
-2.916***
(1.079)
0.315***
(0.081)
11.127***
(0.460)
-2.664**
(1.260)
0.231**
(0.095)
15.575***
(0.537)
-2.636*
(1.397)
0.075
(0.105)
20.378***
(0.596)
702
0.016
36
YES
NO
702
0.006
36
YES
NO
702
0.118
36
YES
NO
702
702
0.065
0.034
36
36
YES
YES
NO
NO
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of log change in real GDP for country i in year t on a binary variable,
f
Di,t
indicating if there is a flight or not, using a local projection framework. Columns (1)-(5) report the local projection for 1 to 5 years out
respectively since the occurrence of a flight for the real GDP growth. Upper panel of the table shows results without any fixed effects, whereas
the bottom panel includes country fixed effects.
Table 2.12: Effect of Flight on Current Account Balance: LP-OLS estimates
VARIABLES
(1)
CA Balance (t+1)
(2)
CA Balance (t+2)
(3)
CA Balance (t+3)
(4)
CA Balance (t+4)
(5)
CA Balance (t+5)
1.853***
(0.000)
-0.099**
(0.012)
-0.005
(0.970)
2.121***
(0.000)
-0.324***
(0.000)
0.099
(0.544)
1.281**
(0.032)
-0.335***
(0.000)
0.195
(0.274)
0.743
(0.237)
-0.381***
(0.000)
0.278
(0.137)
0.194
(0.765)
-0.389***
(0.000)
0.336*
(0.083)
702
0.033
NO
NO
702
0.066
NO
NO
702
0.051
NO
NO
702
0.056
NO
NO
702
0.055
NO
NO
Flight (t+1),
CA Balance (t)
Constant
Observations
R-squared
Country FE
Year FE
92
VARIABLES
Flight (t+1),
CA Balance (t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
CA Balance (t+1)
(2)
CA Balance (t+2)
(3)
CA Balance (t+3)
(4)
CA Balance (t+4)
(5)
CA Balance (t+5)
2.098***
(0.455)
-0.109***
(0.041)
-0.027
(0.128)
2.465***
(0.593)
-0.345***
(0.053)
0.068
(0.166)
1.439**
(0.642)
-0.361***
(0.057)
0.180
(0.180)
0.717
(0.668)
-0.410***
(0.060)
0.279
(0.187)
0.099
(0.683)
-0.418***
(0.061)
0.343*
(0.192)
702
0.066
36
YES
NO
702
0.067
36
YES
NO
702
0.038
36
YES
NO
702
702
0.075
0.059
36
36
YES
YES
NO
NO
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of current account balance in percentage of GDP for country i in year t on
f
a binary variable, Di,t
indicating if there is a flight or not, using a local projection framework. Columns (1)-(5) report the local projection for 1
to 5 years out respectively since the occurrence of a flight for the current account balance in percentage of GDP. Upper panel of the table shows
results without any fixed effects, whereas the bottom panel includes country fixed effects.
Table 2.13: Effect of Flight on Real Exchange Rate: LP-OLS estimates
VARIABLES
Flight (t+1)
Real Exchang
Rate (t)
Constant
Observations
R-squared
Country FE
Year FE
(1)
Real Exchange Rate (t+1)
(2)
Real Exchange Rate (t+2)
(3)
Real Exchange Rate (t+3)
(4)
Real Exchange Rate (t+4)
(5)
Real Exchange Rate (t+5)
9.101***
(0.000)
-0.098***
(0.009)
-1.495**
(0.038)
7.315**
(0.022)
-0.103**
(0.039)
-2.378**
(0.014)
4.754
(0.210)
-0.045
(0.441)
-2.766**
(0.016)
3.945
(0.409)
-0.029
(0.698)
-3.569**
(0.013)
1.823
(0.746)
-0.097
(0.271)
-3.908**
(0.022)
672
0.030
NO
NO
672
0.014
NO
NO
672
0.003
NO
NO
672
0.001
NO
NO
672
0.002
NO
NO
93
VARIABLES
Flights (t+1)
Real Exchange
Rate (t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
Real Exchange Rate (t+1)
(2)
Real Exchange Rate (t+2)
(3)
Real Exchange Rate (t+3)
(4)
Real Exchange Rate (t+4)
(5)
Real Exchange Rate (t+5)
10.767***
(2.562)
-0.142***
(0.038)
-1.661**
(0.726)
8.859***
(3.354)
-0.178***
(0.050)
-2.544***
(0.950)
5.642
(3.929)
-0.144**
(0.058)
-2.880***
(1.113)
5.180
(5.105)
-0.104
(0.076)
-3.706**
(1.446)
3.330
(5.981)
-0.150*
(0.089)
-4.063**
(1.694)
672
0.004
35
YES
NO
672
0.005
35
YES
NO
672
0.045
35
YES
NO
672
672
0.029
0.012
35
35
YES
YES
NO
NO
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of log change in real exchange rate (domestic goods in terms of foreign
f
goods) for country i in year t on a binary variable, Di,t
indicating if there is a surge or not, using a local projection framework. Columns (1)-(5)
report the local projection for 1 to 5 years out respectively since the occurrence of a surge for the log change in real exchange rate. Upper panel
of the table shows results without any fixed effects, whereas the bottom panel includes country fixed effects.
Table 2.14: Effect of Flights on Nominal Exchange Rate: LP-OLS estimates
VARIABLES
Flight (t+1)
Nominal Exchange
Rate (t)
Constant
(1)
Nominal
Exchange Rate
(t+1)
(2)
Nominal
Exchange Rate
(t+2)
(3)
Nominal
Exchange Rate
(t+3)
(4)
Nominal
Exchange Rate
(t+4)
(5)
Nominal
Exchange Rate
(t+5)
10.984***
(0.010)
0.651***
(0.000)
3.132**
(0.020)
8.399
(0.270)
1.169***
(0.000)
8.022***
(0.001)
1.509
(0.889)
1.602***
(0.000)
13.972***
(0.000)
-2.411
(0.861)
1.997***
(0.000)
18.893***
(0.000)
-5.490
(0.738)
2.349***
(0.000)
23.625***
(0.000)
687
0.438
NO
NO
687
0.434
NO
NO
687
0.414
NO
NO
687
0.407
NO
NO
687
0.398
NO
NO
Observations
R-squared
Country FE
Year FE
94
VARIABLES
Flights (t+1),
Nominal Exchange
Rate (t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
Nominal
Exchange Rate
(t+1)
(2)
Nominal
Exchange Rate
(t+2)
(3)
Nominal
Exchange Rate
(t+3)
(4)
Nominal
Exchange Rate
(t+4)
(5)
Nominal
Exchange Rate
(t+5)
13.518***
(4.486)
0.546***
(0.033)
4.563***
(1.367)
11.693
(7.942)
0.935***
(0.058)
11.421***
(2.420)
4.640
(11.165)
1.232***
(0.081)
19.541***
(3.402)
1.756
(14.018)
1.511***
(0.102)
26.193***
(4.271)
-0.112
(16.624)
1.752***
(0.121)
32.584***
(5.066)
687
0.253
35
YES
NO
687
0.244
35
YES
NO
687
0.309
35
YES
NO
687
687
0.290
0.262
35
35
YES
YES
NO
NO
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: The table reports the OLS estimation results of a regression of log change in nominal exchange rate (domestic currency per foreign
f
currency (US dollars)) for country i in year t on a binary variable, Di,t
indicating if there is a fight or not, using a local projection framework.
Columns (1)-(5) report the local projection for 1 to 5 years out respectively since the occurrence of a flight for the log change in nominal exchange
rate. Upper panel of the table shows results without any fixed effects, whereas the bottom panel includes country fixed effects.
Table 2.15: Effect of Flight on Real GDP Growth: LP-IPWRA estimates
ATE
Flight
POmean
No Flight
Flight
[1em] Observations
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-0.884
(0.15)
-0.421
(0.61)
-0.076
(0.94)
-0.184
(0.87)
-1.035
(0.47)
4.221***
(0.00)
8.344***
(0.00)
12.487***
(0.00)
16.607***
(0.00)
20.756***
(0.00)
3.337***
(0.00)
607
7.923***
(0.00)
607
12.412***
(0.00)
607
16.423***
(0.00)
607
19.721***
(0.00)
607
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
95
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of log
f
change in real GDP for country i in year t on a binary variable, Di,t
indicating if there is a flight in capital flows or not, using a local projection
(LP) framework. Columns (1)-(5) report the local projection for 1 to 5 years out respectively since the occurrence of a flight for the real GDP
growth.
Table 2.16: Effect of Flight on Current Account Balance: LP-IPWRA estimates
ATE
Flight
POmean
No Flight
Flight
Observations
(1)
CA Balance (t+1)
(2)
CA Balance (t+2)
(3)
CA Balance (t+3)
(4)
CA Balance (t+4)
(5)
CA Balance (t+5)
-0.303
(0.63)
-0.888
(0.18)
-1.428**
(0.05)
-2.092***
(0.01)
-2.448***
(0.00)
0.100
(0.42)
0.148
(0.39)
0.185
(0.32)
0.228
(0.25)
0.243
(0.24)
-0.203
(0.75)
607
-0.739
(0.25)
607
-1.243*
(0.08)
607
-1.865**
(0.01)
607
-2.205***
(0.00)
607
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
96
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of
f
current account balance in percentage of GDP for country i in year t on a binary variable, Di,t
indicating if there is a surge in capital flows or
not, using a local projection (LP) framework. Columns (1)-(5) report the local projection for 1 to 5 years out respectively since the occurrence
of a flight for the current account balance in percentage of GDP.
Table 2.17: Effect of Flight on Real Exchange Rate: LP-IPWRA estimates
ATE
Flight
POmean
No Flight
Flight
[1em] Observations
(1)
Real Exchange Rate (t+1)
(2)
Real Exchange Rate (t+2)
(3)
Real Exchange Rate (t+3)
(4)
Real Exchange Rate (t+4)
(5)
Real Exchange Rate (t+5)
1.066
(0.80)
-3.620
(0.41)
-6.528
(0.15)
-8.178
(0.11)
-10.553**
(0.04)
-1.210**
(0.04)
-2.252**
(0.02)
-3.665***
(0.00)
-4.389***
(0.00)
-4.512**
(0.01)
-0.144
(0.97)
589
-5.872
(0.18)
589
-10.193**
(0.02)
589
-12.568**
(0.01)
589
-15.064***
(0.00)
589
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
97
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of
f
log change in nominal exchange rate (domestic goods in terms of foreign goods) for country i in year t on a binary variable, Di,t
indicating if
there is a surge in capital flows or not, using a local projection (LP) framework. Columns (1)-(5) report the local projection for 1 to 5 years out
respectively since the occurrence of a surge for the log change in real exchange rate.
Table 2.18: Effect of Flight on Real Exchange Rate: LP-IPWRA estimates
ATE
Flight
POmean
No Flight
Flight
Observations
(1)
Nominal
Exchange Rate
(t+1)
(2)
Nominal
Exchange Rate
(t+2)
(3)
Nominal
Exchange Rate
(t+3)
(4)
Nominal
Exchange Rate
(t+4)
(5)
Nominal
Exchange Rate
(t+5)
-5.437
(0.19)
-15.170***
(0.00)
-20.262***
(0.00)
-23.000***
(0.00)
-25.954***
(0.00)
12.456***
(0.00)
24.118***
(0.00)
34.144***
(0.00)
42.900***
(0.00)
50.729***
(0.00)
7.019*
(0.06)
593
8.948**
(0.04)
593
13.883***
(0.00)
593
19.901***
(0.00)
593
24.775***
(0.00)
593
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
98
Notes: The table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of
f
log change in nominal exchange rate (domestic currency per foreign currency (US dollars)) for country i in year t on a binary variable, Di,t
indicating if there is a flight in capital flows or not, using a local projection (LP) framework. Columns (1)-(5) report the local projection for 1
to 5 years out respectively since the occurrence of a flight for the log change in nominal exchange rate.
Table 2.19: Effect of Surges on Real GDP Growth: LP-OLS estimates
VARIABLES
Surge (MS)
Real GDP Growth(t)
Constant
Observations
R-squared
Country FE
Year FE
99
VARIABLES
Surge (Top 20 percentile)
Real GDP Growth(t)
Constant
Observations
R-squared
Country FE
Year FE
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
0.314
(0.406)
0.452***
(0.000)
2.005***
(0.000)
-0.023
(0.972)
0.620***
(0.000)
5.456***
(0.000)
-1.232
(0.141)
0.768***
(0.000)
9.191***
(0.000)
-2.663***
(0.008)
0.858***
(0.000)
13.188***
(0.000)
-4.020***
(0.001)
0.878***
(0.000)
17.502***
(0.000)
702
0.188
NO
NO
702
0.128
NO
NO
702
0.115
NO
NO
702
0.103
NO
NO
702
0.088
NO
NO
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
1.035***
(0.000)
0.433***
(0.000)
1.667***
(0.000)
0.896*
(0.068)
0.598***
(0.000)
5.130***
(0.000)
0.429
(0.504)
0.740***
(0.000)
8.896***
(0.000)
0.102
(0.895)
0.817***
(0.000)
12.848***
(0.000)
-0.928
(0.298)
0.840***
(0.000)
17.380***
(0.000)
702
0.202
NO
NO
702
0.132
NO
NO
702
0.113
NO
NO
702
0.094
NO
NO
702
0.074
NO
NO
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
s
Notes: The table reports the OLS estimation results of a regression of log change in real GDP for country i in year t on a binary variable, Di,t
indicating if there is a surge or not, using a local projection framework. The top panel reports the results where surges are identified using a three
state Markov switching model using absolute values of the net capital flows. The bottom panel reports the results where surges are identified
by Reinhart and Reinhart (2009). According to their criterion, a surge for a country is defined as the period in which the net capital flow lies
in the top 20 percentile of the country’s own net flow distribution.Columns (1)-(5) report the local projection for 1 to 5 years out respectively
since the occurrence of a surge for the real GDP growth.
Table 2.20: Effect of Surges on Real GDP Growth: LP-OLS estimates with Fixed Effects
VARIABLES
Surge (MS)
Real GDP Growth(t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
100
VARIABLES
Surge (Top 20 percentile)
Real GDP Growth(t)
Constant
Observations
R-squared
Number of WEOCountryCode
Country FE
Year FE
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-0.248
(0.542)
0.349***
(0.000)
2.533***
(0.000)
-1.303*
(0.050)
0.366***
(0.000)
6.736***
(0.000)
-2.665***
(0.001)
0.367***
(0.000)
11.110***
(0.000)
-3.790***
(0.000)
0.299***
(0.002)
15.713***
(0.000)
-4.226***
(0.000)
0.149
(0.158)
20.568***
(0.000)
702
0.103
36
YES
NO
702
0.047
36
YES
NO
702
0.039
36
YES
NO
702
0.032
36
YES
NO
702
0.024
36
YES
NO
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-0.343
(0.248)
0.357***
(0.000)
2.616***
(0.000)
-0.620
(0.201)
0.367***
(0.000)
6.789***
(0.000)
-0.703
(0.251)
0.351***
(0.000)
11.035***
(0.000)
-1.469**
(0.039)
0.291***
(0.003)
15.759***
(0.000)
-1.859**
(0.019)
0.148
(0.171)
20.691***
(0.000)
702
0.105
36
YES
NO
702
0.044
36
YES
NO
702
0.026
36
YES
NO
702
0.016
36
YES
NO
702
0.009
36
YES
NO
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
s
Notes : The table reports the OLS estimation results of a regression of real GDP growth rate for country i in year t on a binary variable, Di,t
indicating if there is a surge or not, using a local projection framework. The top panel reports the results where surges are identified using a three
state Markov switching model using absolute values of the net capital flows. The bottom panel reports the results where surges are identified
by Reinhart and Reinhart (2009). According to their criterion, a surge for a country is defined as the period in which the net capital flow lies
in the top 20 percentile of the country’s own net flow distribution. Columns (1)-(5) report the local projection for 1 to 5 years out respectively
since the occurrence of a surge for the real GDP growth.
Table 2.21: Effect of Surge on Real GDP Growth: LP-IPWRA estimates
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-0.084
(0.30)
-0.504
(0.32)
607
-0.828*
(0.49)
-1.094**
(0.53)
607
-2.051***
(0.67)
-1.454**
(0.66)
607
-3.219***
(0.81)
-2.317***
(0.79)
607
-3.644***
(0.93)
-2.743***
(0.89)
607
ATE
Surge (MS)
Surge (RR)
Observations
ATE
Surge (Top 20 percentile)
Observations
(1)
Real GDP (t+1)
(2)
Real GDP (t+2)
(3)
Real GDP (t+3)
(4)
Real GDP (t+4)
(5)
Real GDP (t+5)
-0.504
(0.12)
607
-1.094**
(0.04)
607
-1.454**
(0.03)
607
-2.317***
(0.00)
607
-2.743***
(0.00)
607
101
∗
p-values in parentheses
p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01
Notes: The Table reports the Inverse Probability Weighting Regression Adjustment Estimator (IPWRA) estimation results of a regression of log
s
change in real GDP for country i in year t on a binary variable, Di,t
indicating if there is a surge in capital flows or not, using a local projection
(LP) framework. The top panel reports the results where surges are identified using a three state Markov switching model using absolute values
of the net capital flows. The bottom panel reports the results where surges are identified by Reinhart and Reinhart (2009). According to their
criterion, a surge for a country is defined as the period in which the net capital flow lies in the top 20 percentile of the country’s own net flow
distribution. Columns (1)-(5) report the local projection for 1 to 5 years out respectively since the occurrence of a surge for the real GDP growth.
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Appendix A
A.1
Markov-Switching Model Results
108
Table A.1: Markov-Switching Result: State Means
country
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
CzechRepublic
Ecuador
ElSalvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
SouthAfrica
SriLanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
Total
Extreme
Flows State
2.5
4.2
0.6
3.5
8.0
3.6
2.9
7.5
30.7
4.3
3.6
3.5
2.4
2.2
3.0
4.4
6.0
5.7
2.4
2.0
3.2
5.7
1.4
4.1
5.2
3.8
11.0
5.7
6.7
1.9
1.6
4.8
2.8
3.4
3.9
6.3
4.6
p-value
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Mean
High Flows
State
1.4
1.0
0.2
1.6
1.7
1.3
1.2
3.2
3.5
1.1
1.7
1.3
0.9
1.0
1.4
1.6
2.3
2.6
1.8
0.7
1.3
1.5
0.6
1.4
1.6
2.1
2.3
3.4
2.1
0.6
0.7
2.6
1.1
1.0
1.2
1.5
1.5
p-value
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Low Flows
State
0.8
0.4
0.1
0.7
0.7
0.4
0.5
1.0
0.9
1.0
0.6
0.8
0.3
0.4
0.4
0.5
1.2
1.8
0.9
0.3
0.6
0.7
0.3
0.9
1.2
0.7
0.8
1.0
1.1
0.2
0.5
1.1
0.4
0.7
1.1
0.5
0.7
p-value
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.57
0.00
0.00
0.00
0.00
0.14
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.08
0.00
0.63
0.03
Notes : The table reports the means and the p-values of the different states, extreme, high, and low
flow states identified by the three state Markov switching model using absolute values of the net
flows series.
109
Table A.2: List of Surge & Flight Episodes by Country from 1980:Q1 to 2014:Q4
Country
Surge Dates
Albania
2004:Q2,
2007:Q2,
2010:Q2,
2013:Q2,
1993:Q2
1981:Q4,
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
Czech Republic
Ecuador
El Salvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
South Africa
Flight Dates
2004:Q4, 2005:Q2, 2005:Q4, 2006:Q2, 2006:Q4,
2007:Q4, 2008:Q2, 2008:Q4, 2009:Q2, 2009:Q4,
2010:Q4,2011:Q2, 2011:Q4, 2012:Q2, 2012:Q4,
2013:Q4, 2014:Q2
2001:Q1, 2009:Q2-Q3, 2012:Q2, 2013:Q2
1996:Q4, 1997:Q4, 2002:Q4, 2004:Q4, 2006:Q4, 2007:Q2,
2007:Q4, 2010:Q3-Q4, 2011:Q1, 2011:Q4, 2012:Q4, 2013:Q1,
2013:Q3-Q4
1994:Q2
1991:Q4, 1994:Q4, 1996:Q4, 1997:Q3, 1999:Q3, 2008:Q3,
2011:Q3
1996:Q4, 2007:Q1
2002:Q1
1991:Q4, 1992:Q4, 1993:Q4, 1998:Q3, 2000:Q2, 2001:Q4
1993:Q1-1995:Q4, 1998:Q1-2001:Q2, 2003:Q1,
2004:Q1-2006:Q1, 2009:Q3
2007:Q3, 2007:Q4, 2008:Q1
1980:Q3, 1981:Q3, 1982:Q3-Q4, 1989:Q1, 1995:Q1,
1997:Q1-Q2, 1991:Q1, 2008:Q3-Q4, 2009:Q3
2005:Q2-2008:Q3
1998:Q3, 2006:Q4, 2007:Q2, 2007:Q4, 2008:Q2
1981:Q2, 1981:Q4, 1991:Q1, 1991:Q4, 1992:Q2, 1994:Q1,
1996::Q3, 1997:Q1, 2009:Q3
2003:Q3, 2004:Q2, 2005:Q1, 2006:Q3, 2013:Q2
2001:Q3
1997:Q4, 2002:Q2-Q3, 2004:Q1, 2006:Q4, 2007:Q4,
2008:Q3-Q4, 2009:Q3, 2011:Q3-Q4, 2012:Q3-2013:Q1
1993:Q3, 1994:Q3, 1996:Q2, 2006:Q1, 2006:Q4, 2007:Q1-Q3
2002:Q2, 2005:Q4, 2011:Q2
1994:Q2, 2007:Q1, 2007:Q4, 2008:Q1, 2010:Q3, 1012:Q1
1994:Q4, 1996:Q2-Q3, 1997:Q2, 2010:Q4
Ukraine
Venezuela
Vietnam
2007:Q1-2008:Q1
Thailand
Turkey
2007:Q1, 2010:Q1
2000:Q3
2007:Q2, 2009:Q3, 2011:Q3
1986:Q4
1998:Q3, 2009:Q1-Q2
1997:Q4-2001:Q3
2014:Q2
1997:Q4, 2008:Q4
2008:Q4-2009:Q2
1982:Q4, 1983:Q4
1994:Q1, 2009:Q2
1999:Q1, 1999:Q3, 2000:Q1-Q2
2002:Q1
1985:Q3
1988:Q1
2005:Q3, 2006:Q4, 2007:Q3
1997:Q1-Q2, 199:Q2-Q3, 2004:Q42008:Q1, 2009:Q2-Q4,
2010:Q3, 2012:Q3, 2013:Q3
1980:Q2-1981:Q2, 1982:Q21983:Q2, 1989:Q3-1990:Q1,
1992:Q41994:Q4, 1997:Q3, 1998:Q2, 1998:Q4, 1999:Q4,
2006:Q2, 2007:Q2, 2009:Q3, 2011:Q2, 2011:Q4-2014:Q2
1985:Q2, 2008:Q1
2004:Q1, 2005:Q1-2006:Q1, 2006:Q4-2007:Q1,
2010:Q4-2011:Q2, 2012:Q2, 2014:Q2
2005:Q4, 2006:Q4, 2007:Q1-2008:Q3, 2012:Q1-2013:Q4
2005:Q1, 2008:Q4
Sri Lanka
1989:Q4, 2002:Q2-2003:Q2, 2003:Q4-2004:Q1
1995:Q3, 1996:Q3, 1997:Q1, 1997:Q3,
1998:Q2, 1998:Q4, 1999:Q2, 1999:Q4,
2000:Q2-Q3, 2001:Q3, 2006:Q3, 2008:Q3,
2010:Q2, 2010:Q4, 2011:Q2, 2011:Q4
2000:Q3, 2008:Q4
1985:Q3, 1986:Q1-Q2
1998:Q3, 2007:Q3, 2009:Q1
1997:Q4, 1999:Q1, 2011:Q4
1998:Q3, 2001:Q2
2004:Q4, 2008:Q4-2009:Q3
1994:Q1-Q2, 2003:Q3, 2004:Q1, 2004:Q3,
2005:Q3-2006:Q3, 2007:Q11-Q2, 2008:Q1-Q3,
2009:Q1, 2010:Q1, 2011:Q1
Notes: The table lists the episodes (in quarters) for each country that are in surges and in flights
in the second and third columns respectively. The surges and flights are identified by a three state
Markov-switching model using absolute values of the net capital flows (in percentage of GDP) for
each country.
110
Appendix B
B.1
Data
I use quarterly data on net private capital flows for a sample of 36 emerging market
economies. The choice of the countries is restricted mostly by availability and quality
of the data. Net private capital flows is the difference between net foreign assets and
net liabilities of the domestic private sector. The accounting method followed by IMF
reports the inflows and outflows from the perspective of the residency of the asset.
I compute the net private capital flows series using net financial account excluding
government liabilities from the IMF’s Balance of Payment Statistics which is similar
to the definition followed by Ghosh et al. (2014). Table B.1 provides the descriptions
and sources of the different data series.
In 2009, the IMF released the sixth edition of its Balance of Payments and International Investment Position Manual (BPM6), replacing the fifth edition (BPM5),
which was in place since 1993. So in order to have a consistent net capital flow series
for the entire sample period, 1980 to 2014, we map the data under BPM6 with BPM5
using the different series under the two methodologies. using the “BPM5-to-BPM6
Conversion Matrix” published by IMF. In BPM6 the signs of inflows and outflows are
111
reversed. I change the sign convention of flows under BPM6 methodology to match
the BPM5 convention.
The data period considered for the analysis starts from the first quarter of 1980
to the second quarter of 2014 for most of the countries in the sample. For some
countries, however, the data was not available for the entire sample period. The data
period considered for each country is also provided in Table B.2. For example for
most of the East European countries in the sample, the data starts in the 1990s.
I control for the size of the economy by taking the net flows as percentage of country’s nominal GDP as for larger economies a large flow may not be a much of concern
relative to a small economy as they may be better in absorbing them. Nominal GDP
data is available only at an annual level for most of the countries in the sample for
the entire period of study. The data is obtained from the IMF’s World Economic
Outlook database. For scaling the quarterly capital flows series, the quarterly nominal GDP data is obtained by interpolating the annual GDP series. In particular, I
use quadratic interpolation to interpolate quarterly data from the annual GDP data.
112
Table B.1: Variables Definitions and Data Sources
Variables
Description
Source
Net capital flows
Net financial account excluding other
investment liabilities in billions of U.S.
dollar (difference between BOP series
codes: “...4995W.9” and “...4753ZB9” for
BPM5 presentation & “.30999FNAA”
and “...3DY00SLGA” for BPM6
presentation
IMF’s BOP
database
Nominal GDP
In billions of U.S. Dollar
IMF’s World
Economic
Outlook
Database
(Version: Oct
2014)
113
Table B.2: Data Period by Country
Country
Start Date
End Date
Albania
Argentina
Bangladesh
Belarus
Brazil
Chile
Colombia
Czech Republic
Ecuador
El Salvador
Guatemala
Hungary
India
Indonesia
Israel
Korea
Latvia
Lithuania
Mexico
Morocco
Myanmar
Nicaragua
Pakistan
Paraguay
Peru
Philippines
Poland
Romania
Russia
South Africa
Sri Lanka
Thailand
Turkey
Ukraine
Venezuela
Vietnam
1995:Q1
1980:Q1
1980:Q1
1996:Q1
1980:Q1
1991:Q1
1996:Q1
1993:Q1
1993:Q1
1999:Q1
1980:Q1
1989:Q1
1980:Q1
1981:Q1
1980:Q1
1980:Q1
1993:Q1
1993:Q1
1980:Q1
2003:Q1
1998:Q2
1992:Q1
1980:Q1
2001:Q1
1990:Q1
1980:Q1
1985:Q1
1991:Q1
1994:Q1
1980:Q1
1980:Q1
1980:Q1
1984:Q1
1994:Q2
1994:Q1
1996:Q1
2014:Q2
2014:Q2
2013:Q4
2014:Q2
2014:Q2
2014:Q2
2014:Q2
2014:Q2
2014:Q1
2014:Q2
2014:Q2
2014:Q2
2014:Q1
2014:Q2
2014:Q2
2014:Q2
2013:Q4
2013:Q4
2014:Q2
2013:Q3
2014:Q1
2014:Q2
2014:Q1
2013:Q4
2013:Q4
2014:Q2
2014:Q1
2014:Q2
2014:Q2
2014:Q2
2014:Q2
2014:Q1
2014:Q2
2014:Q2
2013:Q3
2014:Q1
114