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MODELING AND FORECASTING
INFLATION IN DEVELOPING
COUNTRIES: THE CASE OF ECONOMIES
IN CENTRAL ASIA
Asel ISAKOVA
Discussion Paper No. 2007 – 174
February 2007
P.O. Box 882, Politických vězňů 7, 111 21 Praha 1, Czech Republic
http://www.cerge-ei.cz
Modeling and Forecasting Inflation in Developing Countries:
The Case of Economies in Central Asia
Asel Isakova1
February 2007
Abstract
Inflation constitutes one of the major economic problems in emerging market
economies that requires monetary authorities to elaborate tools and policies to prevent high
volatility in prices and long periods of inflation. The present study outlines some important
macroeconomic developments in the countries of Central Asia with a particular emphasis
on the Kyrgyz Republic. The study focuses on the inflation behavior in Kyrgyzstan and the
main factors that influence price changes in the country. Analysis of inflation and its
relationship with the important macroeconomic indicators is an arduous task due to data
problems (availability, measurement errors, biases, etc.) and complexity of the transition
process experienced by the economy.
Keywords: Central Asia, Kyrgyz Republic, inflation, monetary policy, inflation modeling,
inflation forecast
JEL Classification: E31, E37, E58, P2
1
I am grateful to my supervisor, Prof., RNDr, Jan Hanousek, for helpful suggestions, considerate advice and
support. I am also grateful to Rick Stock for useful comments and discussions. This work was supported by
the CERGE-EI/World Bank Fellowship Program.
1
Introduction
Inflation is considered to be a major economic problem in transition economies and
thus fighting inflation and maintaining stable prices is the main objective of monetary
authorities. The negative consequences of inflation are well known. Inflation can result in a
decrease in the purchasing power of the national currency leading to the aggravation of
social conditions and living standards. High prices can also lead to uncertainty making
domestic and foreign investors reluctant to invest in the economy. Moreover, inflated prices
worsen the country’s terms of trade by making domestic goods expensive on regional and
world markets.
To develop an effective monetary policy, central banks should possess information
on the economic situation in the country, the behavior and interrelationships of major
macroeconomic indicators. Such information would enable the central bank to predict
future macroeconomic developments and to react in a proper way to shocks the economy is
subject to. Thus, studying inflationary processes is an important issue for monetary
economists all around the world. However, it is not an easy task, especially in developing
countries, where economic processes are highly unstable and volatile. Moreover, the
macroeconomic data on developing countries can be unreliable due to many reasons:
measurement error, imperfect methods of measuring, etc.
Nevertheless, there exist a
number of empirical studies on inflation factors in developing countries. These studies
show that inflation is a country-specific phenomenon, and its determinants differ across
countries. Therefore, an effective monetary policy depends largely on the ability of
economists to develop a reliable model that could help understand the ongoing economic
processes and predict future developments. This requires conducting research based on the
data of a particular country that take into account the specific socio-economic context of
that country.
In this regard, this study is important since it is aimed at investigating the
inflationary processes in emerging market economies of Central Asia and, in particularly,
the Kyrgyz Republic. This country is chosen since at the moment almost all data is
available for an empirical investigation. However, the study can be extended for the cases
2
of other economies in the region. The main objective of this discussion paper is to highlight
some important facts about macroeconomic developments in Central Asian countries after
they gained independence and to shed light on inflationary processes in Kyrgyzstan, where
inflation varied considerably over the transition period, and econometric relationships
between prices and other macroeconomic variables appeared to have been unstable during
this time. The research done within the framework of this paper included the collection and
examination of the data available and being appropriate for further empirical investigation.
Moreover, empirical literature related to inflation modeling and forecasting in different
countries was analyzed.
When talking about inflation in transition economies, it is
absolutely important to be aware that inflation is a very complex phenomenon that touches
upon other aspects of socio-economic and political developments. To reveal some longterm relationships between major economic indicators, a cointegration analysis was
performed and a simple model of inflation was developed. However, the results obtained
are preliminary and require further empirical testing and modeling for this study constitutes
a part of the research conducted for my dissertation. When completed the results of the
examination could be an important step forward studying economic processes in transition
economies, and could serve as a motivation and a basis for further investigations by
researchers, economists and policy makers.
Economic Development in Central Asian states after Independence
In 1991, with the collapse of the Soviet Union, the five Central Asian republics of
the
USSR
became
independent
countries.
Kazakhstan,
Kyrgyzstan,
Tajikistan,
Turkmenistan and Uzbekistan faced the challenges of transition from a centrally planned
economy to a market system. Among five economies, Kyrgyzstan was the fastest reformer
in the region taking the advice of Bretton-Woods institutions who advocated a rapid
change.
Comparing the economic development strategies and the performance of the Central
Asian countries constitutes an interesting exercise since all of them started from similar
initial conditions. Before independence they were all administrative planned economies,
3
and were among the poorest Soviet republics. Per capita output estimates in 1990 were at
between 1130 U.S. dollars and 1690 U.S. dollars for the four southernmost republics and
2600 U.S. dollars for Kazakhstan1.
The Central Asian republics played a role of suppliers of primary products – mainly
cotton, oil, and natural gas – and minerals, although the specific resource endowment
varied from country to country.
The dissolution of the Soviet Union in 1991 led to a deep socio-economic crisis in
all five countries that faced three major economic shocks: transition from central planning,
disruption of economic ties with other ex-Soviet republics, and hyperinflation. Table 1
contains the data on annual inflation rates in the five Central Asian economies. Transition
from the centrally planned economy led to severe output decline in all transition economies.
Prices increased very rapidly in 1992, by more than 50 percent per month in all five
countries. The currency became the dominant economic issue in 1993 and four of the
countries introduced national currencies – the Kyrgyz Republic in May, and Turkmenistan,
Kazakhstan, and Uzbekistan in November. Introducing a national currency was a
precondition to put inflation under control. Only Tajikistan could not introduce a national
currency until May 1995 because of the civil war.
The Kyrgyz Republic is considered as one of the most dynamic reformers among
the former Soviet Republics. It has been strongly supported by international institutions
such as the International Monetary Fund (IMF) and World Bank, and was the first Central
Asian country to succeed in fighting hyperinflation, bringing the annual inflation rate below
50 percent in 1995.
As it has been mentioned above, all Central Asian economies suffered a drop in real
output during the first half of the 1990s. This had a negative impact on living standards and
increased social inequality. The Kyrgyz Republic real Gross Domestic Product (GDP)
decline was 45 percent between 1991 and 1995. The way of the so-called “shock therapy”
that implied radical economic reforms and rapid macroeconomic stabilization contributed
to a severe economic recession. However, the advocates of rapid reforming argued that the
1
Pomfret, 2006
4
Kyrgyz economy would start to grow after market economy institutions are built. The
Kyrgyz economy grew by 15 percent between 1995 and 1997, but it is unclear whether this
growth was sound and sustainable. An increase in real GDP was mostly due to one project,
the Kumtor gold mine. In 1998 economic growth slowed down because of the Russian
economic crisis, domestic bank failures, and poor agricultural performance. The economy
exhibited growth by 4.1 percent from 2001 till 2003 and grew by 7 percent in 2005.
However, a major part of the growth experienced is due to Kumtor. The economy is
dominated by agriculture, which accounts for about 36 percent of the real GDP and over 50
percent of the labor force. Kyrgyzstan is relatively limited in natural resources with the
main
resource
being
mountainous
rivers
producing
hydroelectricity.
However,
hydroelectricity production requires large investments that would take long to pay back.
Today gold production makes up 8 percent of real GDP and 25 percent of exports. The
power sector contributes 4 percent to GDP, and energy supply is one of the key inputs for
the development of other sectors1.
The Central Asia economies are open economies if their trade-to-GDP ratios2 are
considered. After the major shocks of the early 1990s, trade recovered in the Central Asian
countries in 1994–1997. Kazakhstan and Uzbekistan had superior export performance
because of the favorable conjuncture in energy and cotton markets between 1997 and 2001.
Turkmenistan’s main natural resource – natural gas – was the major exports item in 19951996 towards CIS countries. However, delays in payments for the gas became the reason
for Turkmenistan to stop its exports in March 1997. This resulted in a decline in Turkmen
exports until in March 1999. Although Kyrgyzstan was comparatively limited in exports,
the country was the first of all the CIS countries to enter the World Trade Organization
(WTO) in 1998.
1
2
IMF, 2006
World Bank estimates, Pomfret, 2006
5
Table 1. Inflation (CPI annual average) (in percent)1
Country
1992
1993
1994
1995
1996
Kazakhstan
Kyrgyzstan
Tajikistan
Turkmenistan
Uzbekistan
1 381
855
1 157
493
645
1 662
772.4
2 195
3 102
534
1 892
180.7
350
1 748
1 568
176.3
43.5
609
1 005
304.6
39.1
31.9
418
992.4
43.1
Country
Kazakhstan
Kyrgyzstan
Tajikistan
Turkmenistan
Uzbekistan
2000
13.2
18.7
32.9
8.3
49.5
2001
8.4
6.9
38.6
11.6
47.5
2002
5.9
2
12.2
8.8
44.3
2003
6.4
3.1
16.3
5.6
14.8
1997
17.1
23.4
88
83.7
70.9
2004
6.9
4.1
7.1
5.9
8.8
1998
7.1
10.5
43.2
16.8
29
2005
7.6
4.3
7
10.7
21
1999
8.3
35.9
27.6
24.2
57.3
2006*
8.7
5.5
9.2
9
19.3
*Data for 2006 are projections
Since this study focuses on the case of Kyrgyzstan, I will discuss the
macroeconomic developments in more details. Curbing hyperinflation did not mean
conducting a sound macroeconomic policy, since macroeconomic management in
Kyrgyzstan was not perfect, as price stability was achieved without bringing the budget
deficit under control. Assistance from multilateral institutions was used to support current
consumption needs rather than to generate future growth. An economic crisis in 1998
following the Russian economic downturn and Kazakhstan’s subsequent devaluation, led to
domestic bank failures. This was followed by currency depreciation, import contraction and
fiscal adjustment in 2000-2001, reduction in external borrowing, and a 2002 debt
restructuring by Paris Club.
During 2000-2005, the country achieved broad macroeconomic stability: with a
more sustainable macroeconomic policy. Economic recovery was helped by robust growth
in Russia and Kazakhstan, and after 2000 labor migration to those two countries and
workers’ remittances became important. Inflation declined steadily to about 4 percent and
1
EBRD, 2006
6
the exchange rate remained stable. Non-gold GDP grew on average by about 4.5 percent
per year in 2000-2005, driven mainly by trade and other retail services, compared with 7.2
percent to other CIS countries.
Table 2. Key Macroeconomic Indicators in Kyrgyzstan, 1991 – 2005 (in percent of
GDP, unless otherwise indicated)1
1991-95
1996-99
2000-05
2001-03
2004
2005
-12.6
5.7
4.0
4.1
7.0
-0.6
-12.5
4.3
4.5
4.4
7.8
1.5
884.0
25.4
6.5
4.0
4.1
4.3
14.9
20.1
19.3
18.5
20.9
21.8
Fiscal deficit
-15.1
-10.3
-5.5
-5.2
-4.1
-4.0
External current
account balance
Exports
-9.6
-17.7
-4.2
-2.8
-3.4
-8.3
32.3
36.9
39.7
38.5
42.6
38.6
Imports
50.1
53.9
47.6
42.8
50.9
57.2
Foreign direct
investment inflows
External debt
3.6
4.4
2.2
1.0
7.9
3.4
40.3
90.9
104.3
107.5
95.2
87.5
10.7
20.2
21.2
24.5
9.7
13.6
Real GDP growth
(in percent)
Nongold growth
(in percent)
Inflation (in
percent)
Gross investment
Debt service to
export ratio (in
percent)
1
IMF, 2006
7
Monetary Policy and Inflation Experience in Kyrgyzstan
The major goal of the policy conducted by the National Bank of the Kyrgyz
Republic (NBKR) is price stability as a precondition for a robust economic growth. When
elaborating monetary policy such factors are taken into account as complexity of transition
processes, small scale of production and high degree of openness of the economy, which
lead to unstable dynamics of money demand. The main instruments of the monetary policy
used by the bank to maintain financial stability are: open market operations, discount rate,
mandatory reserve requirements (MMR), operations of banks refinancing (crediting), and
operations in the foreign exchange market. Open market operations is a tool used to
“sterilize” or replenish the illiquidity in the banking system. These operations include
auctions to sell the NBKR notes, purchase or sale of government securities under REPO
conditions (i.e. with a commitment to sell or buy them back on a specified date in future
and at a price agreed earlier) in the secondary market, direct sale or purchase of government
securities in the secondary market. Discount rate is used as a major benchmark when
determining the value of monetary resources in the economy. MMR are used to regulate the
level of monetary aggregates, bank’s credit and demand for liquidity, as well as a means to
mitigate the risk of insolvency of the banking system. Operations of banks refinancing is an
instrument of a direct impact on money supply used to adjust the level of reserves in
commercial banks in case there is a deficit of financial resources. Forex market operations
are operations with foreign exchange in the interbank foreign exchange market. The role
and efficiency of each instrument mentioned has been changing
along with the
transformation processes in the economy. The monetary policy appeared to be effective in
employing these tools to stabilize prices.
Graph 1 (a) demonstrates the CPI as a percentage to CPI level in December of
previous year. Thus, yearly inflation exceeded 2000 percent in 1992 and was about 1000
percent in 1994. Graphs 1 (a) and (b) plot CPI as percentage to a base year and as a change
to from previous month, respectively, for the period from January 1995 till August 2006.
These graphs help depict high volatility of monthly inflation in Kyrgyzstan and a long-term
pattern of increasing prices.
8
Graph 1. Consumer Price Index (in percent to a given period)1
2400
2000
1600
1200
800
400
0
1992 1994
1996
1998
2000
2002
2004
2006
a) December of previous year = 100 percent
140
110
120
108
106
100
104
80
102
60
100
40
98
20
96
1996
1998
2000
2002
2004
2006
1996
1998
2000
2002
2004
2006
c) Previous month = 100 percent
b) Base year 2000 = 100 percent
As it is evident from the graphs above, CPI was highly volatile during the whole
period from 1992 till 2006. In fact, CPI reached more than 1,400 percent annually in 1992
and 1993 (caused mainly by large increases in fuel costs). In 1994 inflation dropped to
about 180 percent (mainly because of tighter credit and the government's reduced
expenditures).
1
Sources: a) NBKR, b) IMF, and c) National Statistical Committee of KR (NSC)
9
In 1995 the government's inflation target set in cooperation with the IMF was 55
percent, with monthly declines throughout the year. In 1996 the inflation rate was reported
to be 34 percent, 14 percent in 1997 and 18.4 percent in 1998. However, in 1999 prices
increased by more than a half. The reason for such a high inflation level was the
international financial crisis. In 2000, Kyrgyzstan's inflation rate was reduced to a
manageable 9.5 percent (down from 39.9 percent in 1999). Since 2000, the inflation rate
has decreased to about 3 percent in the first quarter of 2005. Annual inflation in 2005 was
4.5 percent, and it was predicted by the NBKR not to exceed this level in 2006. According
to the National Statistical Committee of the Kyrgyz Republic (NSC) CPI constituted 4.1
percent from January to June 2006, and annual rate of inflation was 5.4 percent. The major
factors to influence inflation dynamics were monetary as well as non-monetary: changes in
fiscal policy, in petroleum products prices, tariff policy of natural monopolies, and by
seasonal fluctuations in prices for agricultural products. When studying inflation process in
Kyrgyzstan it is important to take into consideration fluctuations in prices on food since
food products take 50 percent in the composition of consumer goods basket. Graph 2
presents the decomposition of CPI by food, non-food and services index. The data was
transformed into logarithms and the period taken is 1994 till 2006 to make the composition
presentable.
10
Graph 2. CPI decomposition by main sectors (1994 – 2006)
116
115
112
110
108
105
104
100
100
96
95
1994
1996
1998
2000
2002
2004
2006
1994
1996
1998
CPI
2000
2002
2004
2006
2002
2004
2006
Food
112
120
116
108
112
104
108
100
104
96
100
92
96
1994
1996
1998
2000
2002
2004
2006
1994
1996
1998
2000
Services
Non-food products
It is seen in the graph that data exhibits seasonality that is the most pronounced in
case of food products. Seasonality is a usual characteristic of the series taken at monthly
frequency, and it can be dealt with using econometric techniques or software that can be
applied to deseasonalize data.
High degree of dollarization is another factor to be accounted for since dollarization
of deposits, calculated as a share of foreign currency deposits in the total volume of
deposits, was 60.5 percent in the end of June. In the same period dollarization of loans
made up 68.7 percent.
11
Literature Review
There were numerous attempts to model inflation in developed countries as well as
developing countries. For example, Juselius (1992) in her seminal paper on inflation
modeling in a small open economy studied the inflationary processes in Denmark. de
Brower and Ericsson (1998) wrote an appealing paper on inflation modeling in Australia.
Both studies serve as important theoretical and methodological references for later
empirical research in the field of macroeconomic modeling. Welfe (2000) modeled
inflation in Poland, accounting for a number of important features that characterize a
transition economy. Besides these, worth noting are Ramakrishnan and Vamvakidis (2002)
who worked on a model to forecast inflation behavior in Indonesia, and Callen and Chang
(1999) who conducted an empirical study on inflation in India.
Most of the literature in the field constitutes empirical studies for modeling
inflationary processes in different countries and inflation factors in general. These studies
follow approaches based on different economic theories, choosing the most appropriate for
the economy investigated.
Among the studies on modeling inflation, the study by Loungani and Swagel (2001)
is the one that could serve as a starting point for understanding inflation in developing
countries. The authors present stylized facts about inflation behavior in developing
countries, focusing primarily on the relationship between the exchange rate regime and the
sources of inflation. Another important study of inflationary processes was accomplished
by Fischer, Sahay, and Végh (2002) on the experiences of hyper and high inflations in
various countries. The authors found that there is a very strong relationship between money
growth and inflation both in the long and short run.
Golinelli and Orsi (2002) study the inflation processes in three new EU member
countries: the Czech Republic, Hungary and Poland. All three countries possess a similar
historico-economic background and similar economic context: they were administrative
economies before and have undergone major systemic changes during the transition to a
market economy. Investigating inflationary processes in these countries is of great
importance because all countries experienced high inflation episodes during the years of
12
transition, and price stabilization policies played an important role in their successful
transition to a new economic system. The authors follow a methodology very close to that
of Juselius (1992) in modeling inflation behavior in the countries under consideration. They
use the multivariate VAR approach, grouping together those determinants that belong to
main inflation theories: cost pushed inflation, foreign prices and exchange rates, and excess
money. Further, a vector equilibrium correction model specification is used since it enables
capturing short-term dynamics by including stationary variables and past imbalances, i.e.
the “gaps” detected by previous cointegrated relationships.
In his study on the determinants of inflation in Ukraine, the author, Lissovolik
(2003), studies the factors of inflation in Ukraine during the period from 1993 to 2002, the
so-called “transition period”. The most relevant stylized facts important for modeling
inflation behavior in Ukraine appear to be domestic financial instability, external
disequilibria, seasonality of the economy, and allowance for an increase in administered
prices. The resulting equation of an inflation model is a version of a long-term markup of
prices over wages, the exchange rate, administrative prices, short-term factors and dummy
variables.
Oomes and Ohnsorge (2005) are concerned with the problem of significant
dollarization in transition economies, and its impact on the money demand and inflation. In
particular, the authors study the case of Russia, and illustrate that all the measures of money
aggregates that exclude foreign currency are negatively correlated with the nominal
depreciation rate. This could suggest that foreign currency has been an important substitute
for domestic money. The authors estimate an equilibrium correction model (ECM) for
inflation in order to identify how the short-term dynamics of inflation are affected by
deviations from the long-term money demand equation. They found that inflation does not
react significantly to the excess supply of monetary aggregates that exclude foreign
currency.
13
Model and Methodology proposed
Macroeconomic theory suggests different ways to explain the problem of inflation.
The basic concept to be considered is the Phillips curve which reconciles the effects of
“demand-pull” and “cost-push” inflation theories. Originally, the “demand-pull” (or
“inflationary gap”) inflation is generated by the pressures of excess demand as an economy
approaches and exceeds the full employment level of output. Output is generated by
aggregate demand for goods - thus, whatever aggregate demand happens to be, aggregate
supply will follow by the multiplier. However, at full employment output, if aggregate
demand rises, output cannot follow because of full employment constraints. Consequently,
with the multiplier disabled, the only way to clear the goods market is by raising the money
prices for goods.
In contrast, in the “cost-push” theory of inflation or “sellers’ inflation”, the basic
notion is that, in a generally imperfectly competitive economy, firms set prices of output
according to a simple mark-up formula:
p = (1 + m)w
(1)
where m is the mark-up, p price and w wage. When an economy approaches full
employment, the "reserve army of the unemployed" gradually disappears. This will
encourage laborers or their representatives to demand an increase in wages. In order to
prevent this wage increase from eating into profits, employers will subsequently raise
prices and keep the mark-up intact. Of course, if this happens, then workers will not be
making any real wage gains. Perceiving this, they will follow up with another round of
nominal wage increases - which in turn will be followed by a price increase and so on.
Thus, in this version, inflation is a result of this wage-price spiral engendered by the
relative bargaining position of workers in an almost fully employed economy. Thus, the
Phillips curve explanation of inflation states that inflation is correlated with unemployment
and wages. The price of oil is to be included as an externally determined cost that could
have an impact on domestic inflation.
14
Another concept to explain inflation is the monetary school that states that
inflation is related to the excessive monetary growth. In the present study, in the monetary
sector model of inflation would be included the domestic interest rate together with
monetary aggregates, and output. Further, in the context of a small open economy the
external effects are to be added (exchange rate and foreign price indices ).
The methodology proposed is adapted from Juselius (1992) and will basically
consist of two steps as in most previous empirical studies: the Johansen cointegration
procedure will first be employed to reveal the existence of cointegrated series, and if any
cointegrating vector is revealed, the error correction terms will be included in the final
model specification to account for the effect of long-tern deviations in wages, money and
external sector from the equilibrium relationships.
Johansen (1988) cointegration analysis proved to be more usable and effective when
analyzing multivariate systems, which makes it possible to detect if there are more than one
cointegrating vector. The procedure in question relies heavily on the relationship between
the rank of a matrix and its characteristic roots. An intuitive explanation of the procedure is
that it is simply a multivariate generalization of the Dickey-Fuller test.
Data Issues
The data used is monthly, although this frequency could be costly in a sense that
usually less frequent data can be of a higher quality. At the same time, the short time span
of the data available does not allow using quarterly or annual data. Such a tradeoff when
choosing between more observations or better quality of the times series is usually faced by
those studying macroeconomic developments in transition economies. To the issue of a
short time span and high volatility one could also add the presence of measurement errors
or changing measuring techniques that are closely related to the problem of an inflationary
bias. At present, there is no more doubt about the fact that there is a bias in measures of
inflation, especially in the countries that have experienced transition. This bias tends to
overstate the actual inflation and usually arises due to several reasons, among which are
consumer substitution, outlet substitution, quality improvement effect and emergence of
15
new goods. Thus, errors in measuring price indices could be important what further leads to
under-reporting real output1. The very presence of the bias in inflation makes it difficult to
reveal the actual macroeconomic developments, to interpret results obtained and should
always be kept in mind by policy makers. When considering other data issues, one can also
encounter the problem of highly non-stationary series with multiple structural breaks. It
appears, therefore, important to understand the underlying economic developments that
could cause those major changes and, once again, a possible change in the methodology of
computing the indices.
All of the above-mentioned aspects related to the quality of data available are to be
taken into consideration when choosing an appropriate model and methodological
framework for the empirical investigation. The major variable to be used in the present
study as a measure of inflation is CPI. However, PPI should be used as well to check the
robustness of results. In optimal, the measure of core inflation would be appropriate since
the latter excludes possible noisy components of inflation. However, for the present
moment the core inflation is not available as a commonly used indicator for Kyrgyzstan.
Still, there was an attempt to compute this measure given the data on the country2. The data
used in the study is presented in Table 3, where it is obvious that the time span is different
for some time series, and industrial production volume is used as a proxy for GDP since
monthly data is not available for the latter. The main sources of the data are the NBKR, the
National Statistical Committee of the Kyrgyz Republic (NSC) and the Global Financial
Data online database.
1
2
Filer, and Hanousek, 2000, 2003, Moulton, 1996
Uzagalieva, A., 2006
16
Table 3. Data used in the study
Series
Consumer Price Index
(in percent)
Monetary aggregates
(in mln soms):
M1 and M2
Industrial Production
(mln soms)
Rate on REPO operations of the
NBKR (in percent)
Time span
Source
1992:1 till 2006:12
NSC, NBKR
1993:6 till 2006:9
NBKR
1994:1 till 2006:12
NSC
1993:8 till 2007:1
Global Financial Data
Nominal exchange rate (som to 1993:5 till 2006:12
US dollar) (in soms)
Global Financial Data
Oil price (US dollars per barrel)
1992:1 till 2007:1
Global Financial Data
US CPI (in percent)
1992:1 till 2006:10
Global Financial Data
The time span for which all the time series appeared to be available is January 1994
till August 2006. Therefore, all the tests will be performed only for this period of time for
all series.
However, the whole span of the data for CPI was subject to the Vogelsang’s test
that enables to decide if there is a break in the trend function of a time series, and if it is the
case, the test enables to detect the date of the break. The results indicate that there is a
possible trend break in June 1993. This date seems reasonable since the national currency
was introduced just in May that year. In my cointegration testing the time period from
January 1994 is only covered due to unavailability of data for some other series for earlier
dates. Thus, the date of the trend break is not in the chosen sample.
Further, all data was subject to unit root tests to detect the integration order of every
series. Two tests were applied for this purpose: Augmented Dickey-Fuller (ADF) and
Kwiatkowski-Phillips-Schmidt-Shin (KPSS). The two tests are employed to detect
nonstationarity in data generating processes, but differ in stating the null hypothesis. The
17
ADF test poses the null as presence of unit root in data, while the KPSS test defines the null
as stationarity of the time series. It means that the problem is dealt with from opposite
sides. The necessity to apply also the KPSS test is stipulated by the fact that the ADF test
has low power to detect a truly stationary process. In such a situation the tests can give
different results, and this is a task for a researcher to interpret the results obtained and make
a conclusion. Therefore, that would be optimal to combine both tests in unit root testing.
All data has been subject to logarithmic transformation before examination.
Table 4. Stationarity of the series
Series
ADF
KPSS
CPI
Cannot reject
Reject
Differenced CPI
Reject*
Cannot reject
Monetary aggregates
M1, M2
Differenced
M1, M2
Industrial production
Cannot reject
Reject
Reject*
Cannot reject
Cannot reject
Reject
Differenced industrial
production
Central Bank REPO rate
Reject*
Cannot reject
Reject*
Reject
Differenced REPO rate
Reject*
Cannot reject
Nominal exchange rate
Cannot reject
Reject
Differenced exchange rate
Reject*
Cannot reject
Oil price
Cannot reject
Reject
Differenced oil price
Reject*
Cannot reject
US CPI
Cannot reject
Reject
Differenced US CPI
Reject*
Cannot reject
* Reject null hypothesis at 1% significance level
18
The results obtained were somewhat ambiguous for some of the variables.
However, the final conclusions made and presented in the table indicate that both tests gave
almost identical results. CPI, monetary aggregates, industrial production, nominal exchange
rate, oil price and US CPI appeared to be integrated of order one. The only variable that
showed to be stationary by the ADF test in levels at 1 percent significance level was REPO
rate. Still, if an intercept or trend is allowed for, the series is no longer stationary. The
results of the KPSS test show that the rate is non-stationary if an intercept is included. The
first differencing makes the series stationary in both tests. Therefore, the rate is decided to
be treated as being integrated of order one as well.
Long-Run Inflation: Cointegration analysis
As it was described in the section on the model and methodology, three models are
to be tested for cointegration. The Johansen cointegration procedure is used in all three
cases. However, the markup model cannot be tested with all the proposed variables since
the data on wages is not available for the moment. The cointegration test is performed on
the series of oil prices and CPI, which were seasonally adjusted using moving average
techniques before the test together with such series as industrial production, and CPI in the
U.S. These are the data that exhibits seasonality and should intuitively be subject to
seasonal volatility. The republic is dependent on petroleum products imports and, thus, the
prices on oil could have a significant impact on domestic price changes. In the monetary
model the data on CPI, monetary aggregates, interest rates and industrial production are
employed. External sector model includes data on domestic CPI, nominal exchange rate of
som to US dollar, and US CPI.
1) The markup model
A long-run cointegrating relation between consumer prices and oil prices was
estimated using four lags, and allowing for linear deterministic trend.
19
Table 5. Cointegration analysis in the markup model
H0: rank = p
λ
λmax
95%
λtrace
95%
p=0
0.138
21.82
14.07
21.84
15.41
p <= 1
0.0002
0.023
3.76
0.023
3.76
In Johansen cointegration procedure, the λmax statistic tests the null hypothesis that
the cointegration rank is equal to p, against the alternative of p+1 cointegration vectors.
The λtrace statistic tests the null of cointegration of rank p, against a general alternative. In
both tests if a computed test statistic exceeds the critical value, the null is rejected.
In the present case, the results indicate presence of cointegration between oil prices
and CPI at 1 and 5 percent critical levels. The values of the λmax
and λtrace
statistics are such
that the null hypothesis of no cointegration can be soundly rejected. The estimated
coefficient on oil price shows that if the world oil price increases by 1 percent, the CPI will
rise by 0.019 percent. This is small number but it is statistically significant. Speed of
adjustment coefficients that measure the degree to which the variable in question responds
to the deviation from the long-run equilibrium relationship, indicate weak exogeneity of oil
prices. Weak exogeneity stands for the fact that a given variable does not respond to the
discrepancy from the long-run equilibrium relationship. The error-correction term (ECM) is
estimated as a deviation of the actual value of the CPI from the equilibrium one.
2) The monetary model
In this case I have a vector of four variables: prices (CPI), money (M1, M2), income
(industrial production), and interest rates (REPO rate of the NBKR). The results are
presented in the table 6. Again linear deterministic trend is included and four lags of
differenced variables are considered.
20
Table 6. Cointegration analysis in monetary model
a) with M1
H0: rank = p
λ
λmax
95%
λtrace
95%
p=0
0.21
34.77
27.07
49.06
47.21
p <= 1
0.05
7.47
20.97
14.28
29.68
p <= 2
0.04
5.44
14.07
6.81
15.41
p <= 3
0.01
1.37
3.76
1.37
3.76
H0: rank = p
λ
λmax
95%
λtrace
95%
p=0
0.19
31.05
27.07
49.73
47.21
p <= 1
0.07
10.49
20.97
18.69
29.68
p <= 2
0.04
5.46
14.07
8.20
15.41
p <= 3
0.02
2.74
3.76
2.74
3.76
b) with M2
The values of the test statistics in both cases (using M1 or M2) indicate that there is
one cointegrating vector at 5 percent significance level. However, when using M1, the
adjustment coefficients estimated show that the industrial production is possibly weakly
exogenous. If M2 is used, the monetary aggregate and again the industrial production are
weakly exogenous. If the weak exogeneity assumption holds, then it is proper to proceed to
a short-run estimation within a single equation framework. In my case, taking into account
the difference in the results for various measures of the money supply and not completely
studied characteristics of the time series used, that would be reasonable to start with a
single equation for inflation.
21
3) External sector model
This explanation of inflation implies long-run relationship between prices and
exchange rate. A foreign price level can be important in this context, the so-called
“imported” inflation can be an important factor for domestic prices. Three variables are
considered in this case: domestic CPI, nominal exchange rate (som to US dollar) and US
CPI. Linear deterministic trend is allowed for in this model and four lags are used.
Table 7. Cointegration analysis in the external sector model
H0: rank = p
λ
λmax
95%
λtrace
95%
p=0
0.15
24.42
20.97
29.16
29.68
p <= 1
0.02
3.69
14.07
4.74
15.41
p <= 2
0.007
1.05
3.76
1.05
3.76
Trace test statistics show that there is no cointegrating vector at 5 percent critical
level, while maximum eigenvalue statistics implies that one cointegrating vector is present
at 5 percent level. Since the null hypothesis is marginally rejected in the case of the first test
statistics, there is some evidence of the existence of one cointegrating vector. The
adjustment coefficients indicate the weak exogeneity of the exchange rate and the
American price index.
Cointegration analysis indicated existence of long-run relationships in all three
proposed models that explain inflation. Weak exogeneity assumption holds in two models
and is doubtful in the money model. Thus, I would proceed in estimating a single equation
of inflation incorporating the ECM terms estimated in the previous stage.
Short – Run Model Estimation
The model is a distributed lag model with the first difference in consumer price
index as a dependent variable. The analysis starts with four lags of every variable. A new
variable is included, - an output gap – that is computed as a difference between actual value
22
of the industrial production and values obtained by applying Hodrick-Prescott filter to the
series. The lagged variables were sequentially excluded from the model if they appeared to
be statistically insignificant. In table 8 below two models are presented that could possibly
reflect the inflation process in Kyrgyzstan. ∆ stands for the first difference:
Table 8. Estimated coefficients of the model (Dependent variable – ∆CPIt )
Variable
∆ CPI t-1
∆ Ex_rate t
(Kyrgyz som to US dollar)
∆ REPOt-2
Model 1
0.49***
(0.13)
...
R-squared
-0.01 **
(0.003)
-0.02 **
(0.007)
-0.53 **
(0.25)
0.05 *
(0.03)
-0.42 *
(0.25)
0.26
DW - statistic
2.18
(NBKR official rate on REPO operations)
Output gapt-1
ECM1t-1
(cost-pushed inflation)
ECM2t-2
(monetary sector)
ECM3t-1
(external sector)
Model 2
0.46 ***
(0.13)
0.05 *
(0.03)
-0.01 **
(0.003)
...
-0.48 *
(0.25)
0.05 *
(0.03)
-0.45 *
(0.24)
0.25
2.19
*, ** and *** stand for 10, 5 and 1 percent significance levels, respectively
Both models seem sensible and estimated coefficients have the “right” sign. The
past values of inflation appeared to be an important factor that explains inflation. Thus,
there is inertia in inflation behavior. The deviation of the output from long-run pattern
lagged two-periods ahead showed to be statistically significant in the first model. However,
if the nominal exchange rate is included in the model, both the output gap and the exchange
rate are not significant. Therefore, in the second specification, where no output gap is
23
included, a current depreciation of the domestic currency appeared to be a statistically
significant explanatory variable. Thus, current changes in nominal exchange rate also help
explain changes in inflation. The ECM3 term for the external sector proved to be capable to
explain inflation. This indicates the high degree of openness of the Kyrgyz economy what
makes the country sensitive to external shocks. The lagged Central Bank’s instrument rate
appeared to be statistically significant in both models. The intuition behind such a result
could be that if the Central Bank increases the rate, inflation will fall in two periods. This
explanation seems reasonable because the bank uses the policy rate to regulate money
supply. Logically, if interest rate increases, money supply will fall leading to a decrease in
price level. However, one should be prudent when interpreting the results. The statistically
significant relationship between monetary aggregates and inflation still could have not been
found. Two other ECM terms turned out to be statistically significant in both regressions as
well.
To assess and to compare the performance of the two models, a series of diagnostic
tests was conducted. In Table 9, various diagnostic statistics are presented to help establish
the properties of the model and its ability to explain inflation processes.
Table 9. Diagnostic Statistics for the Models (p – values in parenthesis)
Test
Model 1
Model 2
ARCH test
9.38 [0.009]
4.06 [0.131]
Jarque-Bera Normality test
0.37 [0.832]
0.1 [0.977]
Breusch-Godfrey LM test
7.75 [0.021]
8.07 [0.018]
ARCH test for heteroscedasticity shows that there is autoregressive conditional
heteroscedasticity present in residuals in both regressions. Moreover, Breusch-Godfrey
serial correlation test indicates that the residuals are possibly autocorrelated. Thus, the
statistical properties of the models require further testing and consideration of an
appropriate model for inflation process in Kyrgyzstan. The problem of unavailable data,
24
quality of the data used, possible structural changes in some of the series could explain the
difficulty to find stable and statistically significant relationships between the variables used
in the estimation.
The econometric models specified were used for forecasting inflation. The graph
below demonstrates the logarithm of actual inflation and the forecast error in percentage.
Graph 4. Actual inflation and absolute forecast error1
4.70
.006
4.68
.005
4.66
.004
4.64
.003
4.62
.002
4.60
.001
4.58
.000
1996
1998
2000
2002
2004
2006
1996
1998
2000
2002
2004
b) Model 1 (absolute forecast error in percent)
a) Actual inflation (in log)
.007
.006
.005
.004
.003
.002
.001
.000
1996
1998
2000
2002
2004
2006
c) Model 2 (absolute forecast error in percent)
1
Absolute forecast error in percentage is calculated as a difference between actual and predicted values of
inflation divided by actual value of inflation
25
2006
It is evident from the graphs that the absolute error does not exceed 0.7 percent.
Such a small deviation of predicted values from actual ones demonstrates the accuracy of
the forecast. The evaluation of the forecast can be equally performed through analyzing
certain measures of aggregate error, i.e. root mean squared error, mean absolute error or
Theil inequality coefficient. These measures computed for the forecasts performed
appeared to be small in value, and thus, indicating predictive power of the models.
However, once more it should be emphasized that the specifications derived are
preliminary and demand further analysis and improvements.
Conclusion
The purpose of the present study was to highlight macroeconomic developments in
Central Asian economies and to shed some light on inflation processes that have taken
place in Kyrgyzstan since the dissolution of the USSR. The Central Asian countries are
transition economies that have experienced periods of severe economic recession and
structural change. Prices, income, monetary aggregates and other major macroeconomic
indicators exhibited high volatility and non-stationarity during the period examined. The
study required collection of the macroeconomic data and its thorough analysis. In this
respect, it was important to take into account that economic data for transition economies is
usually hard to obtain. Furthermore, the data available could be of poor quality due to
various reasons: missing observations, measurement errors, changes in measurement
methodology, etc. Moreover, it is a known fact that the way inflation is computed often
leads to a bias in its measure. Thus, since indicators of inflation constitute a point of
departure to compute certain macroeconomic indicators, and if, in case of transition
economies, computational errors, high volatility, hyperinflations together with underlying
structural changes are added, there appear more indicators (income, GDP) that should be
doubted in their capacity to reflect the real progress or recession of a given economy.
Therefore, studying inflation, modeling and forecasting its behavior is a challenging
research task.
26
Within the framework of the discussion paper, the data available for the period from
January 1994 till August 2006 were subject to unit root tests to identify the order of
integration. Further, the time series were grouped in the way to reflect the proposed three
explanations of inflation: the cost-pushed inflation, monetary inflation and imported
inflation. All three groups were tested for cointegration to detect long-run relationships
between the variables, and the error correction terms were computed. Two model
specifications of the final inflation were estimated. Such factors as past inflation,
depreciation of the domestic currency, the NBKR REPO rate, the output gap and long-run
deviations in the monetary market, the external sector and world petroleum prices turned
out to explain the variation in current rate of inflation. However, the results obtained in
estimation should be interpreted in the most prudent manner due to complexity of the
economic transformation process.
The models were also used for predicting inflation, and, in fact, the absolute
forecast error was small enough to conclude that the specifications derived have predictive
power. However, for the time being these are preliminary results, and a more elaborate and
advanced empirical investigation is underway for my thesis.
27
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29
Individual researchers, as well as the on-line and printed versions of the CERGE-EI Discussion Papers
(including their dissemination) were supported from the following institutional grants:
•
•
Economic Aspects of EU and EMU Entry [Ekonomické aspekty vstupu do Evropské unie a
Evropské měnové unie], No. AVOZ70850503, (2005-2010);
Economic Impact of European Integration on the Czech Republic [Ekonomické dopady
evropské integrace na ČR], No. MSM0021620846, (2005-2011).
Specific research support and/or other grants the researchers/publications benefited from are
acknowledged at the beginning of the Paper.