MODELING AND FORECASTINGINFLATION IN DEVELOPING

COUNTRIES: THE CASE OF ECONOMIESIN CENTRAL ASIA

Asel ISAKOVA

Discussion Paper No. 2007 � 174

February 2007

P.O. Box 882, Politických vězňů 7, 111 21 Praha 1, Czech Republichttp://www.cerge-ei.cz

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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.

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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

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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 long-

term 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,

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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

5

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 1995-

1996 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 IMF, 2006 2 World Bank estimates, Pomfret, 2006

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Table 1. Inflation (CPI annual average) (in percent)1

Country 1992 1993 1994 1995 1996 1997 1998 1999

Kazakhstan 1 381 1 662 1 892 176.3 39.1 17.1 7.1 8.3 Kyrgyzstan 855 772.4 180.7 43.5 31.9 23.4 10.5 35.9 Tajikistan 1 157 2 195 350 609 418 88 43.2 27.6 Turkmenistan 493 3 102 1 748 1 005 992.4 83.7 16.8 24.2 Uzbekistan 645 534 1 568 304.6 43.1 70.9 29 57.3

Country 2000

2001 2002 2003 2004 2005 2006*

Kazakhstan 13.2 8.4 5.9 6.4 6.9 7.6 8.7 Kyrgyzstan 18.7 6.9 2 3.1 4.1 4.3 5.5 Tajikistan 32.9 38.6 12.2 16.3 7.1 7 9.2 Turkmenistan 8.3 11.6 8.8 5.6 5.9 10.7 9 Uzbekistan 49.5 47.5 44.3 14.8 8.8 21 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

7

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

Real GDP growth (in percent)

-12.6 5.7 4.0 4.1 7.0 -0.6

Nongold growth (in percent)

-12.5 4.3 4.5 4.4 7.8 1.5

Inflation (in percent)

884.0 25.4 6.5 4.0 4.1 4.3

Gross investment 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

-9.6 -17.7 -4.2 -2.8 -3.4 -8.3

Exports 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

3.6 4.4 2.2 1.0 7.9 3.4

External debt 40.3

90.9 104.3 107.5 95.2 87.5

Debt service to export ratio (in

percent)

10.7 20.2 21.2 24.5 9.7 13.6

1 IMF, 2006

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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.

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Graph 1. Consumer Price Index (in percent to a given period)1

0

400

800

1200

1600

2000

2400

1992 1994 1996 1998 2000 2002 2004 2006

a) December of previous year = 100 percent

20

40

60

80

100

120

140

1996 1998 2000 2002 2004 200696

98

100

102

104

106

108

110

1996 1998 2000 2002 2004 2006

b) Base year 2000 = 100 percent c) Previous month = 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)

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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.

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Graph 2. CPI decomposition by main sectors (1994 – 2006)

96

100

104

108

112

116

1994 1996 1998 2000 2002 2004 200695

100

105

110

115

1994 1996 1998 2000 2002 2004 2006

92

96

100

104

108

112

1994 1996 1998 2000 2002 2004 200696

100

104

108

112

116

120

1994 1996 1998 2000 2002 2004 2006

CPI Food

Non-food products Services

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.

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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

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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.

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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.

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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

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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 Filer, and Hanousek, 2000, 2003, Moulton, 1996 2 Uzagalieva, A., 2006

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Table 3. Data used in the study

Series Time span Source

Consumer Price Index (in percent)

1992:1 till 2006:12 NSC, NBKR

Monetary aggregates (in mln soms): M1 and M2

1993:6 till 2006:9

NBKR

Industrial Production (mln soms)

1994:1 till 2006:12 NSC

Rate on REPO operations of the NBKR (in percent)

1993:8 till 2007:1 Global Financial Data

Nominal exchange rate (som to US dollar) (in soms)

1993:5 till 2006:12

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

18

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

Cannot reject Reject

Differenced M1, M2

Reject* Cannot reject

Industrial production

Cannot reject Reject

Differenced industrial production

Reject* Cannot reject

Central Bank REPO rate 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

19

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.

20

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.

21

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

b) with M2

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

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.

22

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

23

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

Model 1 Model 2

∆ CPI t-1 0.49***

(0.13) 0.46 ***

(0.13) ∆ Ex_rate t

(Kyrgyz som to US dollar) ... 0.05 *

(0.03) ∆ REPOt-2

(NBKR official rate on REPO operations)

-0.01 ** (0.003)

-0.01 ** (0.003)

Output gapt-1 -0.02 ** (0.007) ...

ECM1t-1

(cost-pushed inflation)

-0.53 ** (0.25)

-0.48 * (0.25)

ECM2t-2

(monetary sector)

0.05 * (0.03)

0.05 * (0.03)

ECM3t-1

(external sector)

-0.42 * (0.25)

-0.45 * (0.24)

R-squared 0.26 0.25

DW - statistic 2.18

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

24

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,

25

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

4.58

4.60

4.62

4.64

4.66

4.68

4.70

1996 1998 2000 2002 2004 2006.000

.001

.002

.003

.004

.005

.006

1996 1998 2000 2002 2004 2006

a) Actual inflation (in log) b) Model 1 (absolute forecast error in percent)

.000

.001

.002

.003

.004

.005

.006

.007

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

26

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.

27

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.

28

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385

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 aEvropské měnové unie], No. AVOZ70850503, (2005-2010);

• Economic Impact of European Integration on the Czech Republic [Ekonomické dopadyevropské integrace na ČR], No. MSM0021620846, (2005-2011).

Specific research support and/or other grants the researchers/publications benefited from areacknowledged at the beginning of the Paper.