Russian Federation: Selected Issues
Author:
International Monetary Fund
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Publication Date:
04 Apr 2002
eISBN:
9781451833034
Language:
English
Keywords:
ISCR; CR; GDP; ruble broad money; foreign currency; oil price; GDP growth; coincident indicator; natural monopoly; price effect; Real exchange rates; Industrial production; Oil prices; Real effective exchange rates; Real wages; Global; Western Europe
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The Selected Issues paper and Statistical Appendix analyzes output developments in the Russian Federation since the 1998 crisis. It outlines near-term growth prospects for the economy. The paper highlights that output growth accelerated in 1999 and the first half of 2000, but has slowed since then. The initial output recovery was led by import substitution as a result of the large exchange rate depreciation in 1998. One finding in the context of an overall policy package is that the real exchange rate and oil prices were the main determinants of growth after the 1998 crisis.

Abstract

The Selected Issues paper and Statistical Appendix analyzes output developments in the Russian Federation since the 1998 crisis. It outlines near-term growth prospects for the economy. The paper highlights that output growth accelerated in 1999 and the first half of 2000, but has slowed since then. The initial output recovery was led by import substitution as a result of the large exchange rate depreciation in 1998. One finding in the context of an overall policy package is that the real exchange rate and oil prices were the main determinants of growth after the 1998 crisis.

I. Growth After the 1998 Crisis1

A. Overview

1. This chapter analyzes output developments since the 1998 crisis and outlines near-term growth prospects for the economy. 2 Output growth accelerated in 1999 and the first half of 2000, but has since slowed. The initial output recovery was led by import substitution as a result of the large exchange rate depreciation in 1998. Growth was sustained by a large improvement in the terms of trade and a sharp recovery in domestic demand as a result of improved profitability and rising real incomes, while net exports contributed negatively to growth from mid-2000. The recent slowdown of investment and output growth is a result of the real appreciation of the ruble (35 percent since the end of 1999) and decline in oil prices (35 percent since the end of 2000).

A01ufig01

REER, OU Price and Real GDP

(Indices, 1997=100)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

2. Our main findings are that in the context of an overall policy package:

  • The real exchange rate and oil prices were the main determinants of growth after the 1998 crisis.

  • Expected increases in labor costs and continuing real exchange rate appreciation, combined with lower oil prices, are likely to slow growth in the near term.

  • Industrial production is the best coincident indicator for GDP, while the real exchange rate is the most significant leading indicator.

B. Recent Output Developments

Supply side

3. The growth of basic sectors3 of the economy accelerated in 1999 and 2000, but has since slowed. Industry, construction, and freight transportation—which account for an estimated 45 percent of GDP—drove growth of the basic sectors after the crisis, but a slowdown in industry and transportation since the end of 2000 has been the main reason for the slowdown in GDP growth. These sectors are either all tradable or closely linked to investment spending, which has declined since the end of 2000 (see below). In contrast, retail sales, accounting for 8 percent of GDP and being the closest proxy for consumption, have recovered from 2000, indicating buoyant private consumption.

Growth of Basic Sectors, 1998-2001

(Year-on-year percent change)

article image
Source: Goskomstat.

4. At a more disaggregated level, growth in the tradable sectors of industry was high in 1999 and 2000 but has declined since, while growth in non-tradable sectors has remained stable.4 The real exchange rate appreciation, weaknesses in global commodity prices, and the slowdown in the global economy have adversely affected growth of output in the tradable sectors of industry—in particular, the chemical and petrochemical industry and metals and machine-building, which together account for more than a third of industrial production. On the other hand, output of construction materials registered robust growth since the second half of 1999.

Industrial Output, 1999-2001

(Volume, year-on-year percent change)

article image
Source: Goskomstat.
Productivity, profits, and external competitiveness

5. Rapid productivity growth has sustained the improvement in profitability and competitiveness arising from the large depreciation in 1998. Labor productivity in industry increased by almost 40 percent during September 1998-September 2000. While real wages have been rising rapidly since late 1999, profitability in the domestic market—as measured by Product Unit Labor Costs (PULC)5—remains 20 percent higher than before the crisis. External competitiveness—as captured by the Unit Labor Cost (ULC) based REER—improved dramatically after the crisis as a result of the depreciation. Despite a gradual erosion since early 1999, the REER remained about 50 percent below its pre-crisis level in the first half of 2001.

A01ufig02

Productivity, Real Product Wage and Product Unit Labor Cost for Industry

(SA, 1997 = 100)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

A01ufig03

External Competitiveness

(Jan 94=100)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

6. Slower growth of prices of natural monopolies contributed to lower costs in the non-energy sector and supported higher profits and growth. The pattern of a slower adjustment in natural monopoly prices (relative to CPI) after the 1998 crisis continued in 2001 as well. As a result, the energy sector continued to implicitly subsidize the rest of the economy—leading to lower input costs and higher profits, which in turn contributed to higher investment and growth in the non-energy sectors.6

7. Economy-wide profitability also improved significantly after the crisis, further supported by rising global energy prices. Thus, while net profits were negative in 1998, they are now close to 14 percent of GDP, far above the pre-crisis level of 7 percent. This reflects a sharp decline in the number of loss-making enterprises and in the improved profitability of the tradable sector.

Enterprise Profits, 1997–2001

article image
Source: Goskomstat.

Estimated using data for the first half of 2001.

Demand side

8. In 1998–99, growth was driven by net exports, while in the last two years domestic demand has gained in importance. As a result of the sharp real depreciation, net exports led to growth in 1999, with private consumption contributing negatively and the contribution of investment and government consumption being negligible. However, the combination of real exchange rate appreciation and real wage growth since the second half of 1999 led to a decline in the contribution of net exports to growth and an increase in that of consumption. At the same time, high oil prices that increased profits in the energy-exporting sector supported a higher contribution of investment to growth.

A01ufig04

Contribution of GDP Expenditure Components to Growth

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

9. The fiscal stimulus declined after the crisis and turned negative in the first half of 2001. Reflecting improved tax compliance and expenditure restraint, the general government primary balance at constant oil prices has strengthened by more than 2 percent of GDP since 1999. The impact of government consumption on growth thus declined to -0.1 percentage points in the first half of 2001 from 0.6 percentage points in 1998.

10. After a sharp contraction in 1998, real private consumption has increased steadily in line with real wages. Two factors explain the robust growth of private consumption: a fast increase of reported real wages since late 1999 (real wages are the main component of real income though real incomes rose much slower) and an increase in incomes of lower income groups, which was the result of an increase in minimum wages and pensions by the government.

A01ufig05

Retail Sales and Real Wages

(Index, Dec. 93=100, SA)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

Growth of Investment and Profits

(Year-on-year percent change)

article image
Source: Goskomstat.

Share of total.

11. Total investment surged in 2000 on the heels of rapid profit growth but slowed in 2001. Investment was the fastest growing component of GDP in 2000. With retained earnings the main source of investment, this reflected mainly improved profitability—credit from the banking system expanded rapidly reflecting lower real interest rates, but remained a relatively minor source of investment financing. The investment boom was concentrated in the energy and transportation sectors and as such was not clearly related to a general improvement in the investment climate. With profits declining, investment became significantly less buoyant in 2001.

12. Growth of non-energy exports was strong in 2000, but eroding competitiveness of the tradable goods sector has since slowed growth. As a result of the real effective exchange rate appreciation since early 1999, growth of non-energy exports eased substantially in the first half of 2001. Non-energy exports to non-CIS countries declined much faster than to CIS countries reflecting relatively higher growth in the latter. In particular, growth of machinery exports to non-CIS countries was halved while that to CIS countries increased. Light industry exports, facing strong competition, declined for both country groups.

A01ufig06

Ex-Post Real Interest Rates

(In percent)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

Total and Non-Energy Exports

(Volume, year-on-year percent change)

article image
Sources: Central bank of Russia; and Fund staff estimates.

13. Exports of oil and oil products expanded significantly in 2000–01 as new investment eased capacity constraints. Exports of oil and oil products are less sensitive to real exchange rate developments and their growth in the short term is determined mainly by capacity constraints. Increased demand from CIS countries led to a marked increase of exports of these products to these countries. At the same time, the growth of oil exports to non-CIS countries slowed, reflecting lower economic activity in Western Europe.

Export of Oil and Oil Products

(Volume, year-on-year percent change)

article image
Sources: Central bank of Russia; and Fund staff estimates.

14. Imports recovered strongly in the first half of 2000 and their growth rate accelerated subsequently, in line with the real appreciation and recovery in domestic demand (see Box 1). During 1999 and 2000, a switch by consumers to lower quality but cheaper goods from CIS countries led to relatively higher import growth from these countries. However, continuing steady real appreciation has made higher quality goods produced in non-CIS countries more attractive, and growth of imports from these countries exceeded that from CIS countries in the first half of 2001.

Imports by Origin

article image
Sources: Goskomstat; Central bank of Russia; and Fund staff estimates.

Import Demand Function

Imports are an important component of consumption and investment, accounting for 25 percent of GDP. Twenty-five percent of total imports are food products and 29 percent machinery and transport equipment (customs statistics). The estimated share of imported consumption goods in private consumption is more than 30 percent, while the estimated share of imported investment goods is more than 40 percent of total investment.

In the equation below m denotes (log of real imports, y is log of real output, z is log of the real exchange rate and Aug98Dummy is a dummy for August 1998 crisis and Δ is a difference operator.

Δmr=0.17(0.04)Aug98Dummy+3.82(0.73)Δyt−1+0.46(0.12)(0.480.07yt−1−0.480.07zt−1−mt−1),

AdjustedR2=0.76,Standarderror=0.06,Durbin−Watson=1.9

Our estimation results show several important findings. First, the long-run elasticities with respect to both real GDP and the real exchange rate are less than one and of a similar magnitude. Second, in the short run, due to the higher short-term income elasticity, imports are mainly determined by real GDP, with the price effects having only a longer-term impact.1 Third, the import demand function has a relatively high speed of adjustment to its long run equilibrium—it takes approximately one and a half years for a shock to disappear completely. Finally, the results from the Vector Autoregressive Regression (VAR) representation show that the real exchange rate plays an important role in determining real GDP in Russia. Details about the import demand function are given in the appendix.

The estimated income elasticities indicate that the marginal propensity to spend on Imports is very high in the short run (0.5) and declines substantially in the long run (0.22).

The estimated import demand function shows acceptable dynamic and static forecast performance. It correctly captures all qualitative changes in real imports (see the graph) and has a satisfactory root mean squared error of 1 percent for the dynamic forecast.

A01ufig07

Forecasting Imports

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

1 The short run here should be thought as capturing the impact of the explanatory variables on the dependent variable during the next couple of quarters, while the long run defines the level to which the dependent variable will converge in several years.

C. Leading and Coincident Indicators

15. National accounts statistics are reported with substantial time lags and frequently revised, which complicates the evaluation of current and near-term economic activity. If revisions are substantial, initial estimates may not provide sufficient information for policymakers, which poses problems in choosing the appropriate policy mix. Studying leading and coincident indicators may help address these problems.

16. Our analysis shows that the real exchange rate and oil prices are important leading indicators (see Box 2). They affect growth in several ways. First, they have a direct impact through their effect on net exports.7. Second, favorable external factors (high oil prices and prices of raw materials) and depreciation of the real exchange rate have a positive effect on profits and will increase domestic demand (investment) from the export sector, thus stimulating the rest of the economy and supporting higher growth.8 Third, higher investment, other things being equal, will increase the marginal product of labor and hence real wages, which leads to higher private consumption. Finally, a second round effect on growth comes through the impact of investment on next period output and profits.

A01ufig08

REER, Exports and Imports

(Cross Correlations)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

A01ufig09

Oil Price and Exchange Rate vs. Investment

(Cross Correlations)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

17. Investment has produced productivity gain that have translated into increased private consumption. A relatively high and persistent correlation between investment and productivity implies that strong investment during the current period will increase productivity with half a year lag. At the same time, a high correlation between productivity and real consumer wages suggest that productivity pick up leads to an increase in real wages, which in turn will give an impulse to real consumer wages, causing private consumption to increase.

A01ufig10

Investment, Consumer Real Wages and Industrial Productivity

(Cross Correlations)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

The Real Exchange Rate and Oil Prices as Leading Growth Indicators

The short-term direct impact of the real exchange rate and oil prices on growth of industrial production and GDP is captured by the estimated equations given below. In the first equation seasonally adjusted growth of industrial production is explained by changes in the real effective exchange rate and oil prices and in the second equation the same variables explain growth of GDP. Both equations were estimated using quarterly data. The industrial production equation was estimated for the period Q1 1994-Q2 2001 and the GDP equation for the period Q1 1994-Q1 2001.

Industrial production equation:

DLog ( IP ) = − 0.15 ( 0.06 ) DLog ( REER ( − 1 ) ) + 0.12 ( 0.04 ) DLog ( POilUral ) , Adjusted R 2 = 0.26 ,

Standard error = 0.033 , Durbin − Watson = 1.9 ,

where IP is industrial production, REER is the real effective exchange rate and PoilUral is the price of Urals oil.

GDP equation:

DLog ( GDP ) = − 0.10 ( 0.03 ) DLog ( REER ( − 1 ) ) + 0.06 ( 0.01 ) DLog ( POilUral ) , Adjusted R 2 = 0.38 ,

Standard error = 0.018 , Durbin − Watson = 1.6 ,

where GDP is real GDP.

Several important observations flow from the two equations. First, as expected, the real exchange rate and oil price have a bigger impact on the tradable sector (represented by industrial production) than on GDP as a whole. The reason is that services, which account for more than 45 percent of GDP, are not as much directly influenced by the exchange rate as the tradable sector. Second, a comparison of the real exchange rate and oil price elasticities shows that the exchange rate plays a more important role with respect to growth than oil prices. Finally, the two variables explain around 30 percent and 40 percent of the variation of short-term growth in industry and GDP, respectively. This suggests that although the real exchange rate and oil prices explain one third of real GDP growth, there are other important determinants of growth such as technological improvements and structural changes—that are difficult to measure and not included in the equations. For this reason, the above equations should not be used for point estimation of growth, but rather for providing qualitative signals for future growth developments.

18. Our analysis shows that basic sectors and industrial production perform relatively well as coincident indicators.9 Three important characteristics of the two indexes make them useful coincident indicators: they are published monthly and well in advance of the GDP numbers; they represent a high share of GDP (70 percent for basic sectors and around 35 percent for industrial production); and, both predict relatively well GDP growth (see the table for basic sectors results and Box 3 for industrial production results).

GDP Forecast Using Basic Sectors

(Year-on-year growth in percent)

article image
Sources: Goskomstat.

Estimate

19. As an alternative coincident indicator, we studied the Purchasing Manager’s Index (PMI) and assessed econometrically how it predicts industrial production (TP) growth.10 Although we did not find significant correlation between the growth of the PMI and IP (the correlation coefficient was lower than 0.25) we consider the PMI a good qualitative indicator for economic activity, because it captures relatively well turning points in industrial production dynamics. For example, the fall in the PMI prior to the crisis, the slowdown at the end of last year, and the consequent buoyant growth are in line with developments in the IP index.

A01ufig11

PMI vs. IP

(SA, month-on-month percent change)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

A01ufig12

PMI vs. IP index

(PMI-left scale, IP - right scale)

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

20. Leading and coincident indicators suggest that short-term growth prospects for 2002 remain broadly positive although growth is expected to be lower. As a result of the real appreciation, the real exchange rate contributed negatively to growth in 2001 and is expected to continue to do so. Higher oil prices contributed positively to growth in the first three quarters of 2001, but price declines in the forth quarter had a negative impact, which is expected to continue in 2002. The recently announced industrial production growth of 5.1 percent for the first ten months of the year (year-on-year) confirms the positive output developments in late 2001. In addition, high growth in the remaining basic sectors (6.4 percent year-on-year in Q3) suggests that GDP growth has a broader base. The PMI is also indicative of continued growth, although data for November and December show a decline in growth.

Leading and Coincident Indicators

(Qualitative assessment,"+" pro growth. "−" against growth)

article image

Projection

Coincident and Leading Indicators

Main methods

The two most commonly used methodologies to construct coincident and leading indicators are the NBER-Department of Commerce approach (NBER) and Stock and Watson (1989) approach (SW). Both methods construct a composite indicator but differ in their strategy of selecting the indicators.

The NBER approach is based on a scoring system. The system requires that the time series have the following characteristics: economic significance, statistical adequacy, conformity to historical business cycles, smoothness, and currency. These characteristics are then arbitrarily weighted by assigning each characteristic a maximum possible score. Thereafter, a particular indicator is evaluated given how closely it follows each characteristic and an average score is then attached to it. The choice of indicators is based both on the average score and economic judgment.

In the SW approach, time series analysis and econometric techniques (Granger Causality, regression analysis) are used in the selection process. The weights of the indicators are determined using econometric methods. As in the NBER approach, composite indicators are constructed using weighted averages of the selected variables. However, in contrast to the NBER approach, the weights are estimated in the SW approach.

As mentioned earlier, data problems do not allow using the above methods for all potential candidates for leading and coincident indicators. Since industrial production data are the most reliable and have the longest sample, we apply the SW approach to the industrial production index only.

Industrial production as an indicator
DLog ( GDP ) = 0.45 ( 0.06 ) DLog ( IP ) + 0.32 ( 0.1 ) ( 1.6 ( 0.5 ) + 0.6 ( 0.6 ) Log ( IP ( − 1 ) ) − Log ( GDP ( − 1 ) ) )

Adjusted R2 = 0.75, Standard error = 0.01, Durbin-Watson = 2.3

Unit root tests showed that both series are integrated of order one and Engle-Granger tests confirmed a co-integration relationship between the two variables. In addition to this we have evaluated the predictive performance of the estimated equation using “sign statistics” (the ratio of correctly forecasted growth rates to total number of forecasts).

The above equation was used to produce one period ahead forecasts of seasonally adjusted quarterly GDP growth. Actual GDP growth and the forecast together with ± one standard error bands (with a probability of 68 percent growth will be within the bands) are shown in the graph. The “sign statistic” is 0.8, meaning that in 80 percent of the cases the equation correctly predicts the sign of quarterly GDP growth. The standard error implies that the estimates vary within 2 percentage points. For example, a 2 percent estimate of quarterly GDP growth means that with 68 percent probability GDP growth will be between 1 and 3 percent.

A01ufig13

Industrial Production and GDP

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

APPENDIX I: Import Demand Function for Russia

A. Specification and Estimation Methodology

We begin with a prior specification of the import demand function of the following form:

m = f ( y , z , ε )             ( 1 )

where m is a logarithm of the real imports, y is a logarithm of the real GDP, z is a logarithm of the real exchange rate (positive change implies real depreciation) and ε is a random term. In the above specification an increase in real GDP has a positive impact on imports while an increase in the real exchange rate has a negative impact.

Next, we apply the Engle-Granger methodology to test if our prior is consistent with the data and estimate the specified import demand function in an error correction form.

B. Estimation Results

We present here the estimation results for the import demand in Russia in its error-correction form. Standard errors are given in parentheses. Detailed estimation results are given in the technical appendix.

Δ m t = 0.17 ( 0.04 ) A u g u s t 98 D u m m y + 3.82 ( 0.73 ) Δ y t − 1 + 0.46 ( 0.12 ) ( 0.48 ( 0.07 ) y t − 1 − 0.51 ( 0.19 ) z t − 1 − m t − 1 ) ⁢           ( 2 )

In order to take account of the August 1998 crisis we include a dummy variable. The coefficient before the term in the brackets is the speed of adjustment coefficient. The inverse of this coefficient determines how many quarters it takes for 65 percent of the deviation from the long-term equilibrium to be eliminated. In this case we need slightly more than two quarters. The coefficients in the brackets form the co-integrating vector for the import demand function (the coefficient for imports is normalized to one). The short-term import elasticity with respect to the real GDP is 3.82 and the long-term elasticity is 0.48. The long-term import elasticity with respect to the real exchange rate is -0.51.

C. Impulse Response Functions and Variance Decomposition11

In Figure 1 we present the impulse response function from the VAR representation. We use the Cholesky decomposition in order to recover the structural VAR from the reduced form estimated with Ordinary Least Squares (OLS). In order to estimate the structural residuals we use the following restrictions. First, we assume that real GDP is affected contemporaneously by real imports and the real exchange rate; second we suppose that the real exchange rate is affected contemporaneously by real imports; and third we order real imports last implying that they are affected only in the second round from the other two variables.

Figure 1.
Figure 1.

Impulse Response Functions

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

On the horizontal axis we plot the time unit in quarters. On the vertical axis we show the impact of a shock of one standard deviation of the residuals of the corresponding equation in the VAR system. For example, the first column shows the response of real imports, real GDP and the real exchange rate to a shock in the equation for real imports. The full effect of the shocks disappears in approximately one and a half year. The other two columns represent the reaction of the three variables to shocks in real GDP and the real exchange rate.

The results in Figure 2 show that real GDP and the real exchange rate explain a substantial amount of the variation in the import demand in Russia with real GDP accounting for almost 50 percent and the real exchange rate for 25 percent of the variation.

Figure 2.
Figure 2.

Variance Decomposition

Citation: IMF Staff Country Reports 2002, 075; 10.5089/9781451833034.002.A001

References

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  • Enders, Walter, 1995, Applied Econometric Time Series, John Wiley&Sons, Inc., US.

  • Iacovicllo, Matteo, 2001, “Short-Term Forecasting: Projecting Italian GDP, One Quarter to Two Years Ahead”, IMF WP/01/109.

  • International Monetary Fund, 2000, “Russian Federation: Selected Issues,” IMF Staff Country Report No. 00/150.

  • Moore, Geoffrey, Julius Shiskin, 1967, “Indicators of Business Expansions and Contractions”, National Bureau of Economic Research, Occasional Paper (New York).

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  • Simone, Alejandro, 2001, “In Search of Coincident and Leading Indicators of Economic Activity in Argentina”, IMF WP/01/30.

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1

Prepared by Emil Stavrev (EU2).

2

For a deeper understanding of growth factors following the crisis, it would have been desirable to perform a more disaggregated analysis of growth; however, the lack of sectoral data restricted our study to the aggregate level. To alleviate the significant problems in the aggregate data—short-time series, measurement problems, and structural changes that affect economic agents’ behavior—standard econometric methods are supplemented with techniques and analysis aimed at identifying important links among the basic macroeconomic variables. The application of robust econometric techniques does not, though, solve all the problems—a major problem remains with the seasonal adjustment, which poses serious difficulties with our growth analysis (for a discussion on seasonal adjustment issues, see Chapter 1, Box 1—IMF (2000).

3

These include industry, construction, agriculture, freight transportation, and retail trade, which account for about 65 percent of GDP.

4

The construction materials industry is considered a relatively less tradable sector, while the remaining industrial sectors are more tradable sectors. This division is based on the weights of the different industries in total trade as reported in customs statistics.

5

Product unit labor cost is defined as unit labor cost deflated by the producer price index. PULC measures the relative profitability of enterprises—a decline of PULC indicates an increase in profitability.

6

For a discussion on regulated prices, see Chapter 1, Box 3—IMF (2000).

7

The REER is highly and positively correlated with real imports and negatively correlated with real exports. The correlation of the REER with imports is much stronger than with exports suggesting that movements in the REER influence net exports mainly through imports.

8

The real exchange rate is persistently and negatively correlated with investment demand, leading investment by approximately half a year. Oil prices also lead investment, but their impact fades within six months (see second graph).

9

The data of the individual components of the five basic sectors start in 1996 and exist as a chain index (year-on-year percent change) and a base index (percent change relative the previous month). However, for the first three years of the sample (1996, 1997 and 1998) the data for December are missing. For that reason continuous and consistent time series are available only since January 1999, which makes the sample very small for reliable econometric analysis.

10

The Purchasing Manager’s Index is published by Moscow Narodny Bank.

11

The impulse response function and the variance decomposition results presented in Figures 1 and 2 are based on the estimated VAR model.

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Cover IMF Staff Country Reports
Russian Federation: Selected Issues
Author:
International Monetary Fund
Volume/Issue:
Volume 2002: Issue 075
Publisher:
International Monetary Fund
ISBN:
9781451833034
ISSN:
1934-7685
Pages:
157
DOI:
https://doi.org/10.5089/9781451833034.002

REER, OU Price and Real GDP

(Indices, 1997=100)

Productivity, Real Product Wage and Product Unit Labor Cost for Industry

(SA, 1997 = 100)

External Competitiveness

(Jan 94=100)

Contribution of GDP Expenditure Components to Growth

Retail Sales and Real Wages

(Index, Dec. 93=100, SA)

Ex-Post Real Interest Rates

(In percent)

Forecasting Imports

REER, Exports and Imports

(Cross Correlations)

Oil Price and Exchange Rate vs. Investment

(Cross Correlations)

Investment, Consumer Real Wages and Industrial Productivity

(Cross Correlations)

PMI vs. IP

(SA, month-on-month percent change)

PMI vs. IP index

(PMI-left scale, IP - right scale)

Industrial Production and GDP
Figure 1.

Impulse Response Functions
Figure 2.

Variance Decomposition