Download The Applicability of Inflation Targeting in ASEAN Countries

The Applicability of Inflation Targeting in ASEAN Countries
Yong-Shen Lee* and Wai-Ching Poon**
School of Business, Monash University Sunway Campus, Selangor, Malaysia.
Abstract
The paper aims to investigate the applicability of inflation targeting (IT) in ASEAN countries,
focusing on the role of the real exchange rate and exchange rate volatility, and the central banks’
reaction functions. Results illustrate that both IT and non-IT ASEAN countries response
significantly to inflation gap; but neither IT nor Non-IT groups respond significant to the output
gap in setting the interest rates. Comparatively, the role of real exchange rate is more significant
in Non-IT countries than in IT countries. IT countries appear to follow a “mixed strategy” as both
inflation and real exchange rate are important determinants when it comes to setting of interest
rates. Results demonstrate that inflation targeters have lower exchange rate volatility compared to
non-inflation targeters, which implies that IT does not seem to come as a “cost” to domestic
economy with respect to higher exchange rate volatility.
Keywords: Inflation targeting, real exchange rate, exchange rate volatility, ASEAN countries
______________________________
* School of Business, Monash University Sunway Campus, Jalan Lagoon Selatan, 46150 Bandar
Sunway, Selangor, Malaysia. Email: [email protected] Tel: +60123492727.
** Corresponding author.
School of Business, Monash University Sunway Campus, Jalan Lagoon Selatan, 46150 Bandar
Sunway, Selangor, Malaysia. Email: [email protected] Tel: +603 5514 4908.
1. Introduction
Following the instability of money demand condition, the formation of monetary policy
framework has become a serious issue to be discussed by the monetary authorities since the 1980s,
and due to high economics cost, intermediate exchange rate regime is preferred to hard peg regime1.
Many industrialized countries such as United Kingdom, Canada, United States, Germany and
Switzerland have adopted nominal money growth targeting to maintain price stability by focusing
on the deviations of money growth from the pre-announced target, and imposing some
accountability mechanism to preclude large and systematic deviations from monetary targets
(Mishkin, 2000). Although monetary targeting has been proven to be successful in some countries
(e.g. Switzerland and Germany), monetary targeting is eventually abandoned by other countries (e.g.
United States and Canada) (Mishkin, 2000) following its frequent failures in hitting monetary
targets and the instability between inflation and money growth. To overcome the limitation of
previous monetary regimes, inflation targeting (IT) has become the dominant monetary policy
framework for both developing and industrialized countries since the early 1990s (Epstein and
Yeldan, 2009). The announcement of explicit IT adoption during the last two decades has been said
to be the most important change in the monetary policy framework since the collapse of Bretton
Woods system in the early 1970s (Amato and Gerlach, 2002). While IT adoption was pioneered by
New Zealand (1990), followed by Canada (1991), United Kingdom (1992), Sweden (1993), Ireland
(2001) and etc., serious Asian financial crisis-hit Emerging Market Economies (EMEs) countries
such as South Korea (1998), Thailand (2000), Philippines (2002) and Indonesia (2005) have
adopted IT as their monetary rule to maintain price competitiveness in the global economies. The
growing adoption of IT framework is mainly due to the success of IT in creating a credible and
transparent anchor of monetary while allowing the flexibility of the monetary policy in response to
the short term cyclical fluctuations in the economy without jeopardizing the credibility of the bank
(Lin & Ye, 2007). IT monetary policy framework emphasizes on the public announcement of
official quantitative target ranges for the inflation rate at one or more time horizons. With the target
range, it ensures that the inflation rate returns to its target as a result of shocks and the appropriate
monetary policy response to inflation (Freedman & Otker-Robe, 2010). Therefore, IT is adopted as
a nominal anchor for monetary policy framework to tame inflation rate, maintain its price stability
and enhance economic growth, while ensuring the predictability, accountability and transparency of
the central banks.
While many studies discuss about the advantages and disadvantages of IT framework compared
to other monetary regimes, conditions for successful IT in both developed and developing countries
and issues behind IT regime; others evaluate the effectiveness and efficiency of IT framework as a
monetary policy in both developed (eg. Mishkin & Posen, 1997; Debelle, 1997; Johnson, 2002) and
developing countries (eg. Jonas, 1998; Jonsson, 1999; Gottschalk & Moore, 2001), focusing on the
macroeconomic outcomes between IT versus non-targeting countries; or the central bank behavior
in an IT environment. There is substantial mixed empirical evidence in the evaluation of IT
framework, and conclusion on whether IT works is not substantial. While many argued that IT
worked well in controlling the inflation rate and inflation variability (eg. Corbo, Landerrretche &
Schmidt-Hebbel, 2001; Debelle, 1997; Mishkin & Posen, 1997), some argue otherwise (e.g.
Kadioglu, Ozdemir and Yilmaz, 2000; Ball and Sheridan, 2003). When an IT regime is adopted,
there is an important concern about the ability of the Central Bank to control for inflation rate under
the presence of fiscal or external shocks (Galindo and Ros, 2008) such as exchange rate volatility.
1
A number of countries have switched from fixed to a more flexible exchange rate regime. For example, Mexico has switched to a more flexible
crawling peg regime following Tequila Crisis in 1994. Similarly Brazil has started to adopt flexible exchange rate regime in 1999 due to the
inflexibility of the hard-peg exchange rate system; and both Brazil and Mexico adopt IT in the year 1999 (Rojas-Suarez, 2003).
Hence, the extent to which an increase in exchange rate volatility increases the cost of IT, is of our
interest. On the other hand, ASEAN developing countries are argued to have imperfect regulated
financial markets, exchange rate movements with large potential external shocks, weaker fiscal
condition and monetary institutions. Therefore the effectiveness of IT framework in ASEAN
countries remains debatable (Calvo & Mishkin, 2003). The question of whether IT is an effective
monetary framework for ASEAN developing countries remains unclear and controversial. This is
the objective of this study to fill the gap by making exploration to the feasibility of IT in ASEAN
countries within the context of the ASEAN central banks‟ reaction behavior using panel estimation
by adopting a modified Taylor rule operating procedure to accommodate for inflation gap, output
gaps and the real exchange rates.
Past studies have investigated the differences in IT and non-IT countries using “Taylor rule”.
Many of these studies focus on advanced industrial countries and find that countries follow different
policy rules in IT regimes compared to non-IT countries. Limited studies address the applicability
of inflation targeting in ASEAN countries, and most studies focus on a single country, for instance
Poon and Tong (2009) examine the feasibility of inflation targeting in Malaysia. Emerging markets
economies (EMEs), with different institutional arrangements, is differ from industrial countries,
particularly with respect to the credibility and political independence of the central bank, exposures
to different domestic and foreign shocks, and different levels of financial environment. Fraga et al.
(2003) argue that IT in EMEs has been quite successful, but not as successful in comparison to
developed economies because of the challenges associated with weak institutions, limited
credibility and large external shocks. Real exchange rates are likely to play a very significant and
important role in the formulation of optimal monetary policy rule in developing countries (Aghion
et al., 2009; Aizenman et al., 2011; Edwards, 2006; Taylor, 2001). The cost of adopting IT is higher
for countries with relatively less developed financial sectors since they are more likely to suffer
output losses as a result of high level of exchange rate volatility (Aghion et al., 2009). Central banks
in EMEs are likely to follow a monetary policy rule based on Taylor rule that captures not only
some form of target inflation and output deviations from the natural rate but also on the real
exchange rate fluctuations (Aizenman et al., 2011). Therefore, it is substantial interesting to
examine the association between the adoption of IT and exchange rate volatility in both IT
(Indonesia, Philippines and Thailand) and Non-IT (Brunei, Cambodia, Malaysia, Myanmar,
Singapore and Vietnam) in ASEAN, which allows the distinction between the characteristics of the
monetary policy rule under the IT and Non-IT groups.
2. Theoretical Framework
Taylor (1993) offers dynamic pattern of monetary policy rules and it describes monetary policy
reaction function. Taylor suggests the use of short term nominal interest rate when there is any
deviation of inflation rate from the pre-set inflation target and/or deviation of actual output from its
potential level. When the inflation level deviates higher than the targeted level or the actual output
rises beyond its potential level, an increase in the short term nominal interest rates will be adjusted
to eliminate the deviation of predicted future inflation from targeted and/or to expel the actual
output from the potential output, vice versa. Hence, the short term nominal interest rate serves as an
instrument to ensure that the inflation rate will be on target and all possible sources for production
in the economy will be materialized in the production process. The simple Taylor rule equation is as
follows:
i = if + π + h (π- π*) + g (
)
(1)
Where i, if, π, π*, y, and y* denote short term interest rate, lagged interest rate, actual inflation
rate, target inflation rate, actual GDP or GDP growth, potential GDP or potential GDP growth,
respectively. h is inflation rate reaction coefficient, and g is GDP gap coefficient. Both sizes of
coefficients h and g indicate the preferences of policy makers. When the actual inflation (π) is equal
to inflation target (π*), and output level (y) is equal to its potential (y*), the short term nominal
interest rate (i) is equals to the sum of real interest rate (if) and actual inflation (π). The values of
coefficients will be of no important. If coefficient g is valued zero and coefficient h is valued high,
the policymakers focus on the deviation of inflation, and neglected the output gap. This is a pure
Monetarist rule. Meanwhile for pure Keynesian rule, when the coefficient h is zero and coefficient g
is valued high, the policy makers emphasis on the output gap and not the deviation of actual
inflation. Taylor (2001) suggests that exchange rate has zero coefficient for a closed economy and
non-zero coefficient for an open economy. It is appropriate to include exchange rate in the monetary
policy reaction function for open economies in ASEAN countries because of their pass through
effects on the inflation rate. Aizenman et al. (2011) examine the central banks‟ response to the
inflation gaps and output gaps, and find that real exchange rate plays a significant role in the central
bank policy in both IT and non-targeting EMEs. They claim that EMEs IT targeters are not
following „pure‟ IT framework, but they also attempt to stabilize the exchange rates. Aizenman et
al. find that IT countries place more weight in inflation when setting the interest rates and also
conclude that IT countries do not follow a pure IT strategy, but rather a mixed-IT strategy whereby
Central bank respond to both inflation and exchange rate.
3. Literature Review and Propositions
Kadioglu, Ozdemir, and Yilmaz (2000) discuss the applicability and prerequisites of IT in
developing countries by analyzing the general aspects of IT regime in developed countries and the
scope of IT in developing countries. Results reveal that the preconditions of IT in many developing
countries are less satisfactory and that lacking of powerful models restricts them to make effective
inflation forecasts. By making a reference to Chile, Mishkin (2000) explains the reasons why IT
may not be appropriate for many EMEs. These reasons include i) weak central bank accountability
that cause long lags effect on the inflation outcome, ii) financial instability caused by flexible
exchange rate, iii) fiscal dominance and high degree of dollarization in EMEs.
According to Aizenman et al. (2011), there are two main empirical approaches to examine the
performance of IT. The first approach focuses on the macroeconomic outcomes of countries,
comparing IT and non-targeting countries. Mixed results are obtained in extant literatures. For
example, Lin and Ye (2007) examine the impact of IT on both developed and developing countries
for the year 2007 and find that IT has no significant impact in 7 inflation targeting industrial
countries (Australia, Canada, Finland, New Zealand, Spain, Sweden and the UK). However, using
the same model, Lin and Ye (2009) re-examine the impact of IT on 13 IT developing countries
(Brazil, Chile, Columbia, Czech Republic, Hungary, Israel, South Korea, Mexico, Peru, Philippines,
Poland, South Africa, and Thailand). Results reveal that IT has significant impact on the inflation
and inflation variability on developing countries, which is differed from 2007‟s ones. Johnson
(2002) examines the effect of IT with respect to the behavior of expected inflation in 11 industrial
countries, and finds that despite a decrease in inflation rates in IT countries; neither the variability
of expected inflation nor the average absolute forecast errors fall after implementing IT. Mishkin
and Posen (1997) also evaluate the impact of IT on levels and persistence of inflation, and they find
that IT has been a very effective strategy to curb inflation, especially in maintaining the benefits of
registering low inflation levels without having substantial effects on output growth for three IT
adoption countries (i.e., New Zealand, Canada and the United Kingdom). Similar results are also
obtained by Debelle (1997), who compares the average inflation levels for seven IT countries (New
Zealand, Canada, United Kingdom, Finland, Sweden, Australia and Spain) with the G7 countries.
Debelle finds a much steeper decline in inflation in these seven IT countries, and concludes that IT
is useful for countries that are lack of anti-inflation credibility. In supportive of Debelle (1997),
Corbo et al. (2001) also conclude that IT countries have been able to meet their inflation targets and
reduce inflation volatility. Similarly Pétursson (2004) concludes that adoption of IT does reduce the
fluctuations of inflation when he compares the average standard deviation of actual inflation in 5
years before the introduction of IT corresponding to the following year of IT introduction. By
extending Ball and Sheridan (2005)‟s study, Goncalves and Salles (2008) examine the impact of IT
on a subset of 36 EMEs (13 are IT adopter countries, 23 countries are non-IT adopters) using cross
sectional OLS. They find that IT adopting countries have significant greater positive reduction in
inflation, contribute in lowering growth volatility and enhance economic growth compared to nonadopting countries. This result is consistent with Fraga et al. (2003) who state the non-adopting IT
countries tend to have higher inflation rate and inflation volatility compared to adopting countries.
The second empirical approach uses to evaluate IT policies focuses on central bank behavior
under inflation targeting and non-targeting and how they operate in an IT environment. Again there
is mixed evidence on issue whether formal adoption of an IT regime in advanced industrial
economies substantively changes the behavior of central banks in response to inflation and output
gaps. Many economists distinguish the differences between IT and non-IT in term of central bank
reaction function by explicitly estimating “Taylor rule” equations for individual country. For
example, Corbo et al. (2001) find mixed evidence for 17 individual OECD countries and conclude
that inflation targeters exhibit larger inflation gap coefficient relative to the output gap coefficient,
although in most cases the coefficients are not statistically different from zero. Meanwhile Lubik
and Schorfheide (2007) estimate a calibrated small-scale general equilibrium model for Australia,
Canada, New Zealand and the United Kingdom in 1983 using Taylor-type rules to examine the
respond to output, inflation and exchange rates. They find that not all countries respond to the same
gap, for instance Australia and New Zealand change interest rates in response to exchange rate
movements, but Canada and the United Kingdom do not. Because there is mixed evidence on the
behavior of central banks, therefore two propositions are tested to investigate the ASEAN central
banks‟ responses to inflation gap and output gap. We propose two propositions. First, the Central
Bank‟s reaction function on interest rate depends on Inflation Gap, and second, the Central Bank‟s
reaction function on interest rate depends on Output Gap.
Using Taylor rule to investigate Australian models, Dennis (2003) finds inflation should not be
the only focus when it comes to setting interest rate, he finds that authorities should also focus on
the real exchange rate fluctuations when setting the interest rates. Unlike the great bulk of studies
on the central bank reaction function for developed countries, there are limited empirical studies
focus on the central bank reaction function in EMEs. For instance, Schmidt-Hebbel and Werner
(2002) make a comparison between three IT adopters‟ experiences of Brazil, Chile and Mexico by
applying VAR models. They estimate Taylor rule equations for each country with the real interest
rate as the dependent variable. They find mixed evidence with respect to setting the interest rates, in
which only Brazil shows statistically significant with respect to the expected inflation gap, and only
Chile shows statistically significant with respect to the output gap. They also find that the trade
surplus (lagged) enters significant negative in most cases (i.e. trade surplus leads to a decline in the
real interest rate) and that this effect dominates all other variables. They find that these countries
continue to respond to exchange rate changes in the short-term, if not the medium-term, and
characterize them as “dirty” floaters. Mohanty and Klau (2004) also estimate modified Taylor rule
for 13 emerging economies by complementing inflation gap, output gap, lagged interest rates with
current and lagged real exchange rate changes using OLS estimation. They find that the coefficients
on real exchange rate changes are statistically significant in 10 countries, in which the policy
responses to exchange rate changes is frequently larger compared to the response to inflation and
the output gap and this supports the “fear of floating” hypothesis. It seems that the central bank
response not only to the inflation and output gaps when setting the interest rate, they may response
to real exchange rate. According to De Mello and Moccero (2008), out of the four Latin America
countries (Brazil, Chile, Columbia and Mexico), they find that IT has been associated with stronger
and persistent response to expected inflation especially in Brazil and Chile. Only Mexico responses
significantly to the changes of nominal exchange rate when it comes to the central bank‟s reaction
function during the IT period. Similarly Aizenman et al. (2011) find that both the IT and non-IT
groups response significantly to the real exchange rate when they examine the central banks
behavior. Therefore, the third proposition is to examine if Central Bank‟s reaction function on
interest rate depends on real exchange rate.
The examination of exchange rate in a monetary policy rule is important as policy maker is
concerned about the consequences of the exchange rate volatility (Aizenman et al., 2011). Aghion
et al. (2009) conclude that exchange rate volatility indeed reduces the productivity of a developing
country. They argue that the negative consequences of exchange rate volatility are more severe in
developing countries compared to developed economies. Example for negative consequences of
exchange rate volatility is the rising cost of funds in circumstances where agency and contract
enforcement costs are prevalent. Rose (2007) show on how IT affects exchange rate volatility. Rose
(2007) examines the exchange rate volatility for 45 IT and non-IT countries and results show that
exchange rate volatility is typically lower for IT countries compared to non-IT countries. Hence, the
IT adopters do not seem to come at the expense of higher exchange rate volatility. To examine how
substantial the adoption of IT affecting the exchange rate volatility, we propose the fourth
proposition to be exchange rate volatility in IT ASEAN countries is lower compared to Non-IT
ASEAN countries.
4. Methodology
4.1 Model specification, data and variables (ASEAN’s Central Banks Reaction
Behaviors)
Following Aizenman et al. (2011), we adopt Taylor (1993) rule to examine the Central Bank‟s
response to the inflation gap, output gap and external variables (i.e., the real exchange rate) using
fixed-effect balanced panel approach for both IT and non-targeting countries. A monetary policy
reaction function is as follows:
it = ρit-1 + α (yt – y*) + β (πt – π*) + γXt
(2)
Where it, it-1, πt, π*, yt, y*, and Xt, denote as nominal interest rate, lagged nominal interest rate,
actual inflation rate, target inflation rate, GDP output rate, potential GDP rate, and real exchange
rate change, respectively. ρ, α, β and γ denote as lagged interest rate reaction coefficient, GDP gap
reaction coefficient, inflation gap reaction coefficient and real exchange rate change reaction
coefficient, respectively.
There are a few assumptions in this paper. First, it is assumed that the Central Bank‟s reaction
function on interest rate depends on both inflation gap and output gap, and assumes that external
variable (the Real Exchange Rates Change) is part of the policy reaction function. In addition,
following Aizenman et al. (2011) and English, Nelson, and Sack (2002), the lagged nominal interest
rate is also included in the estimation equation. According to English et al. (2002), lagged interest
rates should be included when estimating a policy rule because it helps to reduce serially correlated
errors that reflect the omission of various episodic factors from the policy rule.
Following the estimation equation adopted by Aizenman et al. (2011), the estimation equation in
this study can be written as follows:
ii,t = ρii,t-1 + α (yi,t – yi) + β (πi,t) + γXi,t + δAFCi,t + εi,t
(3)
Where ii,t, ii,t-1, πi,t, yi,t, yi, Xi,t and δAFCi,t, are denoted as nominal interest rate, lagged nominal
interest rate, inflation gap, output rate, potential output rate, real exchange rate change, dummy
variable that is 1 for the Asian Financial Crisis, and 0 for Non-Asian Financial Crisis. ρ, α, β, γ and
δ are lagged nominal interest rate reaction coefficient, output gap reaction coefficient, inflation gap
reaction coefficient, real exchange rate change reaction coefficient and dummy variable coefficient
respectively.
Nine ASEAN countries are selected in my study, in which three are inflation targeters countries
(Indonesia, Philippines and Thailand), and six are non-targeters (Brunei, Cambodia, Malaysia,
Myanmar, Singapore and Vietnam) over the annual data from 1990 to 2010. Laos is excluded from
my study due to data unavailability. We refer to Rose (2007) and Mishkin and Schmidt-Hebbel
(2007) for the exact start date of the adoption of IT for these countries, and the information is
available in Appendix 1. Different nominal interest rates (i) are used to measure the interest rates.
Money market nominal interest rate is used for Thailand; lending nominal interest rates for Brunei
and Cambodia; central bank policy nominal interest rates for Indonesia, Myanmar and Singapore;
and Treasury bill rates is used for Malaysia, Philippine and Vietnam. The real output rate (yt) is
measured as nominal gross domestic products (GDP) divided by domestic consumer price index
(CPI), whereas the GDP gap is measured as the difference between the logarithmic of the real
output rate and logarithmic of potential output rate. The potential output rate (y*) is measured using
a Hodrick-Prescott (HP) filter with a 100 smoothing parameter (Aizenman et al., 2011; Corbo et al.,
2001; Lubik and Schorfheide, 2007). The inflation rate (πt) is expressed as the current year CPI
minus previous year CPI divided by previous year CPI%, the inflation gap (π*) is the difference
between the inflation rates and the trend target rate. The inflation target rates for the three IT
ASEAN countries are obtained from the national Central Banks‟ websites, whereas there is no
target rate for non-IT ASEAN countries, instead it will be substituted by the trend inflation rate that
are derived using the HP filter with 100 smoothing parameter (Corbo et al., 2001). The real
exchange rate (Xt) is calculated as the logarithmic of nominal rate times the US CPI divided by
domestic CPI. For robustness check, a dummy variable (1=AFC period, 0 otherwise) is added to
test whether Asian Financial Crisis (AFC) causes a change on central bank‟s reaction behavior. All
data are collected from the International Financial Statistics (IFS) 2011 CD-ROM, except for the
target rates for the three IT ASEAN countries are obtained from their official central banks‟
websites respectively. More details on data description are available in Appendix 2. The codes for
variables use in this study are available in Appendix 3.
4.2 Model specification, data and variables (Measurement of Exchange Rate
Volatility)
Do IT countries impose a “cost” in the form of substantially higher exchange rate volatility
compared to non-IT countries? To answer this question, exchange rate volatility between ASEAN
IT-adopters and non-IT adopters will be examined using annual data from 1990 to 2010. Following
Rose (2007), an intercept and a set of control variables are included. The OLS model can be written
as follows:
Vol(ner)it = βITit + α +
it
+ εit
(4)
Where Vol(ner)it is the volatility of the effective exchange rate for country i over period t, ITit is
a dummy variable with 1 if i is an inflation targeter over period t and 0 otherwise; ε is a wellbehaved disturbance term, and α and δ are the nuisance parameters. Four control variables (Xit) are
included. These control variables are (1) The current account (expressed as a percentage of GDP);
(2) The natural logarithm of openness (exports plus imports as a percentage of GDP); (3) Log
population; and (4) Log PPP-adjusted real GDP per capita (Rose, 2007). Exchange rate volatility is
measured as (current year nominal exchange rate minus last year nominal exchange rate) divided by
last year nominal exchange rate (McKenzie, 1999). Regress the exchange rate volatility on a binary
dummy variable (1 for IT adopter country, and 0 otherwise) using an Ordinary Least Squares (OLS).
The control variables data are obtained from the World Bank‟s World Development Indicators
(WDIs) 2011 CD-ROM. The exchange rates database is obtained for the IFS 2011 CD-ROM.
5. Findings
5.1 Descriptive Statistics and Correlation Matrix
Tables 1 and 2 describe the main variables that are used in the study and also the descriptive
statistics, correlation and covariance matrix for nine ASEAN countries. Table 1a and 2a show the
descriptive statistics for IT and non-IT targeters, respectively. Table 1b and 2b show the correlation
and covariance matrix for IT and Non-IT targeters, respectively. The real GDP rate for Non-IT
countries (12.25%) is higher than IT countries (8.34%), whereas the inflation rate on average is
slightly lower for IT countries (6.37%) compared to Non-IT countries (6.64%). Although the
difference in mean value for inflation rate between IT and Non-IT is small, the standard deviation
of IT is virtually half of the standard deviation of the Non-IT countries. This indicates that the
dispersion of the inflation rate in IT countries is much smaller than Non-IT countries. Clearly this
indication is consistent with the objective of IT that IT targeters will keep the dispersion within the
target rate boundaries. For Non-IT countries, there are no target rate boundaries; hence the
dispersion of inflation rate tends to be higher. Furthermore, the maximum values for both inflation
rate and output rate are around 20.21 and 0.84 respectively, lower compared to the maximum value
for Non-IT countries. The nominal interest rate for Non-IT is 2.6 point lower than IT countries.
According to Fisher‟s equation, nominal interest rate is the difference between the inflation rate and
the real interest rate. After rearranging Fisher‟s equation, real interest rate is the difference between
the nominal interest rate and inflation rate. From Table 1a, the average inflation rate and nominal
interest rate for IT countries are 6.37% and 9.81%, respectively. Using Fisher‟s equation, the
average real interest rate is valued at 3.44%. Whereas for non-IT countries, the average inflation
rate and nominal interest rate are 6.64% and 7.22%, respectively (Table 1b), thus the average real
interest rate is valued at 0.87%. This indicates that the average real interest rate is 2.86% higher in
IT countries compare to Non-IT countries. IT countries in ASEAN also appear to have a higher
average real exchange rate change (5.48%) compared to Non-IT countries (3.16%). This indicates
that IT countries exercise less exchange rate management compared to Non-IT countries (Aizenman
et al., 2011).
From Table 1a and 1b, it can be observed that there are strong correlation between nominal
interest rate and real exchange rate in both IT and Non-IT ASEAN countries, with correlation
values of 0.42 and 0.64 for IT and Non-IT countries, respectively. This is consistent with Aizenman
et al. (2011)‟s findings that real exchange rate plays a crucial role in a country‟s central bank policy
rule. However, it is noted that the correlation values for IT targeters are lower than the Non-IT
targeters. The possible explanation is that IT countries attempt to stabilize the real exchange rate by
using interest rate, however due to their commitment to target inflation, making their actions to
stabilize the real exchange rate to be less flexible.
From the correlation matrix, it can also be deduced that the correlation relationship between
inflation gap and nominal interest rate for IT countries is stronger compared to Non-IT countries.
This indicates that the three IT countries are “indeed IT countries” as they place the stabilization of
inflation rate and the controlling of inflation rate as their priority when it comes to the setting of
interest rates compared to the other Non-IT countries. The dummy variable seems to be highly
correlated to the nominal interest rates for IT countries. The correlation value between the dummy
and AFC is 0.18 for Non-IT countries, which is much lower for IT countries of 0.47. This indicates
that the impact of the 1997/98 AFC does play a significant role in influencing the Central Banks‟
behavior in setting the interest rate for the three IT ASEAN countries. For output gap, both IT and
Non-IT countries show a weak correlation between the nominal interest rate and output gap, which
are 0.1525 and -0.119 for IT and Non-IT, respectively.
Philip-Perron (PP) unit root test is employed to examine the time-series properties of the
variables. The lag length is based on the Akaike Info Criterion (AIC) and the maximum lag length
is 4 is applied. Result reveals that the first differencing, I(1), is sufficient to remove any nonstationarity in the variables. Results are not reported here but are available upon request.
Table 1a: Summary Statistics: IT countries in ASEAN
Variable
Source
Nominal
Interest rate
Real output
rate
Output Gap
IFS
Author‟s calculation using IFS Gross Domestic
Product (GDP)
Author‟s calculation (Difference between output rate
and potential output rate)
* Potential output rates calculated using HP filter
Author‟s calculation using CPI data from IFS
Inflation
rate
Inflation
Gap
Real
Exchange
Rate
Author‟s calculation (Difference between inflation
rate and target or trend inflation rates)
* Target rate obtained from national Central bank
sources
*Trend inflation rates calculated using HP filter)
Author‟s calculation using national currency per US
dollar
Mean
Minimum
Maximum
9.811
Standard
Deviation
6.592
1.205
38.440
8.338
5.245
-1.471
15.266
-0.0016
0.744
-2.736
3.395
6.374
5.223
-0.855
36.864
0.419
4.112
-8.176
24.706
5.480
2.564
3.270
9.488
Note: Countries, N=3 (Indonesia, Philippines and Thailand). Annual data (1990-2010), Observation = 63.
Table 1b: Correlation & Covariance Matrix: IT countries in ASEAN
Covariance
Correlations
Probability
Nominal
Interest Rate
Nominal
Interest rate
42.768
1.000
--10.472
0.392
0.00150
0.252
0.0525
0.685
6.954
0.418
0.001
0.892
0.465
0.001
Inflation Gap
Output Gap
Real Exchange
Rate
Dummy
Inflation Gap
Output Gap
16.650
1.000
--0.336
0.112
0.384
0.291
0.02801
0.8273
0.370
0.310
0.0137
0.545
1.000
--0.00314
0.00182
0.989
-0.0166
-0.0767
0.550
Real Exchange
Rate
Dummy
6.47
1.000
--0.0191
0.0256
0.842
0.0862
1.000
---
Note: Countries, N=3 (Indonesia, Philippines and Thailand), annual data: 1990-2010. Observation = 63.
Table 2a: Summary Statistics: Non-IT ASEAN countries
Variable
Source
Mean
Nominal
Interest rate
Real output
rate
Output Gap
IFS
Author‟s calculation using IFS Gross
Domestic Product (GDP)
Author‟s calculation (Difference between
output rate and potential output rate)
* Potential output rates calculated using
HP filter
Author‟s calculation using CPI data from
IFS
Author‟s calculation (Difference between
inflation rate and target or trend inflation
rates)
* Target rate obtained from national
Central bank sources
*Trend inflation rates calculated using HP
filter)
Author‟s calculation using national
currency per US dollar
Inflation rate
Inflation Gap
Real
Exchange
Rate
Minimum
Maximum
7.215
Standard
Deviation
5.463
0.2725
18.800
12.249
2.050
8.802
16.111
1.146
2.945
-0.104
9.134
6.638
10.963
-2.315
57.075
-0.0684
6.391
-26.179
31.849
3.165
3.506
0.175
9.752
Note: Countries, N=6 (Brunei, Cambodia, Malaysia, Myanmar, Singapore and Vietnam). Annual data (1990-2010),
Observation = 90.
Table 2b: Correlation & Covariance Matrix: Non-IT ASEAN COUNTRIES
Covariance
Correlation
Probability
Nominal
Interest Rate
Inflation Gap
Output Gap
Real Exchange
Rate
Dummy
Nominal Interest
rate
29.518
1.000
--1.756
0.0510
0.634
-1.885
-0.119
0.266
12.194
0.644
0.000
0.285
0.175
0.0992
Inflation Gap
Output Gap
40.388
1.000
---0.246
-0.0132
0.902
-0.896
-0.0404
0.7053
0.405
0.212
0.0444
8.575
1.000
---3.116
-0.305
0.00340
-0.0302
-0.0344
0.7476
Real Exchange
Rate
12.156
1.000
---0.0235
-0.0224
0.8338
Dummy
0.0900
1.000
---
Note: Countries, N=6 (Brunei, Cambodia, Malaysia, Myanmar, Singapore and Vietnam). Annual data (1990-2010),
Observation = 90.
5.2 Taylor Rule Regressions
Table 3 presents the estimation for the Taylor rule regression using an unbalanced panel
estimation procedure. Columns (1) and (5) present the benchmark models without the dummy
variable and the external variable (real exchange rate change), while Columns (2) and (6) present
the benchmark model with the dummy variable. Columns (3) and (7) present the benchmark model
with real exchange rate change, without the dummy variable. Lastly, columns (4) and (8) present
the benchmark model with both real exchange rate and the dummy variable for IT and non-IT
respectively. Based on the adjusted R-square values, about 44 percent to 90 percent of the
variability in nominal interest rate can be explained using the model in this study. The degree of
persistence, given by the lagged interest rate coefficients is quite high and significant. It is noted,
however, that the persistence level in Non-IT group is higher than IT group.
Table 3: Taylor Rule: Baseline Model
IT
Non-IT
(4)
(5)
(6)
(7)
(8)
Lagged Interest
0.441***
0.930***
0.924***
0.841***
0.829***
Rate (t-1)
(4.521)
(25.504)
(24.911)
(19.715)
(18.985)
Inflation Gap
0.324**
0.0513*
0.0467
0.0540*
0.0457
(0.0282)
(1.663)
(1.464)
(1.900)
(1.529)
Output Gap
1.195
0.0151
0.0194
0.0764
0.0871
(1.555)
(0.220)
(0.279)
(1.138)
(1.288)
Real
0.505**
0.239***
0.251***
Exchange Rate
(2.083)
(3.400)
(3.528)
Dummy (AFC)
7.154***
7.390***
0.431
0.782
(3.481)
(3.657)
(0.598)
(1.145)
Observations
62
62
62
62
86
86
86
86
Adjusted R2
0.443
0.532
0.462
0.558
0.900
0.885
0.900
0.899
AIC
6.073
5.912
6.052
5.869
4.097
4.116
3.987
3.995
F-test
17.150
18.358
14.112
16.415
220.662
164.289
189.543
152.493
Note: Dependent variable: Nominal interest rates. Unbalanced panel estimation. The associated t-statistics are noted
below each estimated coefficients. ***, **,* indicate the significance value at 1, 5 and 10 percent, respectively.
Variables
(1)
0.572***
(5.833)
0.465***
(3.016)
1.100
(1.284)
(2)
0.539***
(5.832)
0.312**
(2.108)
1.343*
(0.0940)
(3)
0.491***
(4.610)
0.480***
(3.162)
0.960
(1.132)
0.472*
(1.766)
It is observed that the coefficients for inflation gap are highly significant and larger for the IT
group (range from 0.31 to 0.47), however not generally in Non-IT regime (coefficients range from
0.046 to 0.051). This implies that a one percentage point increases in the inflation gap, the nominal
interest rate is expected to increase between 0.31 to 0.47 percentage points for IT group and 0.046
to 0.051 for Non-IT group. This result for inflation gap is consistent with Aizenman et al. (2011).
The output gap, on the other hands is generally not significant for most models (output gap can only
be seen significant in column 2). This result is consistent with result obtained by Aizenman et al.
(2011) which see no significance in any of the regressions.
It exhibits that both IT and non-IT group respond significantly to the real exchange rate when
setting interest rates. The coefficients for real exchange rate are large and highly significant. This is
consistent with the results obtained by Aizenman et al. (2011) and Mohanty and Klau (2004) that
support the real exchange rate is an important variable to factor into the central banks‟ policy
function. The coefficients, however, are more significant in the Non-IT countries compared to the
IT countries. One possible explanation could be due to IT countries attempt to “lean against the
wind”, which implies that although the IT group attempt to minimize the exchange rate fluctuations
by using the interest rates, these countries‟ actions are constrained by their commitment to target for
inflation in comparison with Non-IT can pro-actively pursue the objective of stabilizing the
exchange rate (Aizenman et al., 2011). Therefore, it supports the third proposition, in which real
exchange rate does play a significant role in setting of the interest rate.
Results show that the dummy variable is highly significant in the IT group, but not significant
for the Non-IT group. This could be due to these IT group (Indonesia, Philippines and Thailand) is
severely hit during the 1997/98 Asian Financial Crisis in comparison to countries such as Vietnam
and Myanmar, thus making the dummy variable to be highly significant in the regressions for the IT
group. Although the coefficients for inflation gap are highly significant for IT group, however when
dummy is included in the regression, there is a decrease in the coefficients for inflation gap. For
example, the coefficient of inflation gap without dummy is 0.465 percentage point (column 1),
however, the coefficient of inflation gap with dummy experiences a 0.153 percentage point drop to
0.312 percentage (column 2). On the other hands, the coefficients for the real exchange rate with
dummy have been increased. For instance, the coefficient of real exchange rate change without
dummy is 0.472 percentage point at 10 percent level of significance (column 3); however the
coefficient of inflation gap with dummy experiences a 0.033 percentage point increase to 0.505
percentage (column 4) at 5 percent level of significance. This phenomenon applies the same to both
IT and Non-IT groups. For example, the coefficient of inflation gap without dummy for Non-IT
group is 0.051 percentage point (column 5), the coefficient of inflation gap with dummy
experiences a 0.005 percentage point drop to 0.047 percentage (column 6). In the similar vein for IT
group, with dummy, the coefficients for the real exchange rate increase; without dummy the
coefficient of real exchange rate change is 0.239 percentage point (column 7). Similarly analogy,
with dummy variable the coefficient of inflation gap experiences a 0.012 percentage point increase
to 0.251 percentage (column 8).
5.3 Exchange Rate Volatility
According to Rose (2007), a negative coefficient indicates that exchange rate volatility is indeed
lower in IT countries compared to Non-IT countries. There are 6 rows of results in Table 4 in which
the first is the default estimation, followed by 5 rows of robustness checks. Results show that the
coefficient for the default estimation is negative, although it is not significantly different from zero.
This implies that exchange rate volatility is actually lower for IT ASEAN countries compared to
Non-IT ASEAN countries. The default specifications is tested by successively dropping all four
control variables, namely log PPP-adjusted real GDP per capita, log population, current account and
log openness. Although the coefficients are also insignificant different from 0, the striking feature is
that all coefficients for the robustness checks also show negative coefficient values. The result is
consistent with Rose (2007)‟s findings that show all coefficients for the regression estimation for
exchange rate volatility are negative values. In a nutshell, the exchange rate volatility for IT
ASEAN countries is basically lower than Non-IT ASEAN, although all the coefficient values are
not significantly difference from 0. In other words, the adoption of IT as the primary monetary
policy does not seem to come as a “cost” to the domestic economy in the form of higher exchange
rate volatility.
Table 4: Exchange Rate Volatility in IT and Non-IT ASEAN Countries
Volatility Interval
Default
No controls
Without log PPP-adjusted real GDP per capita
Without log population
Without current account
Without log openness
Nominal
-0.0420
(-1.000)
-0.0430
(-1.001)
-0.0477
(-1.600)
-0.0500
(-1.136)
-0.0274
(-0.626)
-0.0443
(-1.100)
Note: Coefficients tabulate are OLS coefficients from regression of exchange rate volatility on inflation targeting
volatility. Controls variables that are not reported but included are (a) current account (as percentage of GDP), (b)
natural logarithm of openness (trade as percentage of GDP), (c) natural logarithm of population, and (d) natural
logarithm of real PPP-adjusted GDP per capita. All series are annual series obtained from World Development
Indicators 2011 CD-ROM. The associated t-statistics are noted below each estimated coefficients. ***,**,* indicate the
significance value at 1, 5 and 10 percent, respectively. Table 4 reports the coefficients for the dummy variables, along
with the associated T-stats values.
6. Conclusion and Policy Implications
Following Aizenman et al. (2011), the applicability of IT practices is examined using 3 IT and 6
Non-IT ASEAN countries by employing panel estimation from 1990 to 2010. Results show that
both IT and non-IT ASEAN countries response significantly to inflation gap when setting the
interest rates; however neither IT nor Non-IT groups respond significant to the output gap in setting
the interest rates. Furthermore, results also show that domestic focus on the publicly announced IT
strategies by central banks in ASEAN is different from non-IT countries. Among the IT ASEAN
central banks respond significantly to the real exchange rate when it comes to the setting of interest
rates. In particular, IT ASEAN countries do not follow a “pure” IT strategy, but rather a mixed IT
strategy that takes into account the real exchange rates when setting interest rates. This observation
could imply that policy makers in IT see the change of real exchange rate as a predictor for future
inflation that may influence the monetary policy reaction functions. IT countries in ASEAN still
emphasize on publically announced IT policies closely. For instance, the primary objective of the
Bangko Sentral ng Philinas (BSP) is to maintain stable prices conducive to balance economic
growth (Bangko Sentral ng Philinas, 2010), and to promote and preserve monetary and
convertibility of the Philippines peso (Mariano & Villanueva, 2005). In Indonesia, after the 19971998 AFC, the primary objectives of Bank Indonesia are to stabilize the rupiah and to have a low
and stable inflation rate (Bank Indonesia, 2008). Meanwhile there are two main monetary policy
goals for the Bank of Thailand, i.e., to maintain low inflation and stable exchange rates (Bank of
Thailand, 2008). By contrast, result indicates that neither the IT nor the Non-IT ASEAN countries
place significant weight in output when setting the interest rates.
Comparatively, results show that the role of real exchange rate is more significant in Non-IT
countries than in IT countries. One explanation for this could be due to the fact that although the IT
group attempts to minimize the exchange rate fluctuations by using the interest rates, these
countries reaction behavior might be constrained by their commitment to target inflation in
comparison with Non-IT that may pro-actively pursue the objective of stabilizing the exchange rate
(Aizenman et al., 2011). Results illustrate that real exchange rate places a significant role in the
setting of interest rate for Non-IT ASEAN countries. This is also consistent with the objectives of
central banks of Non-IT countries. For instance, two main goals of the central bank of Malaysia
states are to maintain a low and stable of inflation rate and to maintain monetary stability i.e., the
stability of the Malaysian Ringgit (Bank Negara Malaysia, 2011). Furthermore, results also indicate
that the exchange rate volatility is typically lower for IT countries compared to non-IT ASEAN
countries. This result is consistent with Rose (2007). Results from this study reveal that the
adoption of IT as a monetary policy does not seem to come as a “cost” to the domestic economy in
the form of higher exchange rate volatility. Hence, the six Non-IT ASEAN countries should follow
the footsteps of their IT counterparts in adopting IT as the exchange rate volatility in IT ASEAN
countries is proven to be lower compared to Non-IT ASEAN countries. Therefore, out of the four
propositions, only the second proposition which states central banks do take into account output gap
when setting the nominal interest rates is not supported, whereas the other three propositions are
supported.
Other macroeconomic variables or non-economic factor such as employment rate, money supply,
industrial production, and political influences may have a significant impact on the adoption of IT
have not been considered. For future study, the applicability of IT in ASEAN countries, focusing on
the macroeconomic outcomes of countries, as compared to non-targeting countries would be an area
to be explored.
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Appendices
Appendix 1: IT Start Dates
IT ASEAN Countries
Indonesia
Philippines
Thailand
Start of IT Regimes
July 2005
January 2002
May 2000
Non-IT ASEAN Countries
Brunei
Cambodia
Malaysia
Myanmar
Singapore
Vietnam
Source: Rose (2007) and Schmidt-Hebbel (2007).
Appendix 2: Data Descriptions
Variables
GDP growth rate
GDP (Output) Gap
Inflation rate
Inflation Gap
Interest rate
Real exchange rate
change
Calculations
The growth rate of GDP is the percentage change in real GDP from one year to the next.
The general fromula to calculate GDP growth rate is as followed:
GDP growth rate =(Current Year GDP- Last Year GDP)/Last Year GDP x 100%
For example, the GDP growth rate for the period 2004-2005 is as follows:
GDP growth rate = [GDP(2005) - GDP(2004)]/ GDP(2004) × 100
GDP growth rate- Potential GDP growth rate
 Potential GDP growth rate is calculated using a Hodrick-Prescott Filter
Log CPI
Log CPI- Log of the target rates
 Target rates obtained from central banks
Nominal interest rate
Log real exchange rate
Appendix 3: Codes for Variables Used in Study
Variables
Nominal
Interest Rate
Inflation Rate
GDP
Real Exchange
rate
Brunei
Cambodia
Indonesia
51660P..ZF
…
51664…ZF
…
51699B..ZF
…
516..AE.ZF
…
52260P.FZ
F
52264…ZF
…
52299B..ZF
…
522..AE.ZF
…
53660…Z
F…
53664…Z
F…
53699B..
ZF…
536..AE.Z
F…
Countries
Myanma
r
54860C..
51860…Z
ZF…
F…
54864…Z 51864…Z
F…
F…
54899B..
51899B..
ZF…
ZF…
548..AE.Z 518..AE.Z
F…
F….
Malaysia
Philippin
es
56660C..
ZF…
56664…Z
F…
56699B..
ZF…
566..AE.Z
F…
Singapor
e
57660…Z
F…
57664…Z
F…
57699B..
ZF…
576..AE.Z
F…
Thailand
Vietnam
57860B..ZF
…
57864…ZF
…
57899B..ZF
…
578..AE.ZF
…
58260C..
ZF…
58264…Z
F…
58199B..
ZF…
582..AE.Z
F…
Source: International Monetary Fund’s International Financial Statistics (IMF) 2011 CD-ROM.