Abstract
This Selected Issues paper examines the potential costs of faster disinflation in Russia, drawing on the experience of European transition countries. The paper analyzes this experience, discusses factors contributing to the persistence of moderate inflation, and quantifies the disinflation costs in these countries. It compares the Russian economy with the sample countries. The paper concludes that a relatively rapid reduction of core inflation from above 10 percent in 2002 to less than 5 percent in 2004 would be beneficial for the Russian economy.
III. The Equilibrium Real Exchange Rate in a Commodity Exporting Country: The Case of Russia1
A. Introduction
1. Over the last decade, Russia has been on an economic roller-coaster. The start of transition saw a sharp output collapse and prolonged stagnation. This was followed by a tentative recovery in 1997, and a dramatic banking and exchange-rate crisis in August 1998. During 1999-2002, a depreciated real exchange rate (RER), booming world prices for energy (Russia’s key export), a tightened fiscal policy, and significant progress in key structural reforms led to high growth rates, a significant recovery in investment, unprecedented fiscal and external current account surpluses, and rising living standards.
2. Not surprisingly, there is much debate regarding the policies needed to sustain rapid growth and complete the transition. Questions about external competitiveness, and the appropriate exchange-rate policy have featured prominently in this debate. Some observers note that the sharp real appreciation which accompanied the start of the transition was eventually followed by a crisis, and suggest that the rapid post-crisis recovery in the RER, unless strongly resisted, will eventually also endanger growth, employment, and fiscal and external sustainability. In contrast, the recent real appreciation has been accompanied by dramatic swings in the terms of trade and the external current account, moderate increase in productivity and some reduction in the non-oil government deficit. It is therefore quite possible that changes in the RER may have reflected movements in the underlying fundamentals, and indeed that further appreciation might be desirable.
3. Taking a somewhat broader perspective, exchange-rate fluctuations can have significant effects, for at least two reasons. First, even short-term RER volatility can impose large welfare costs. Especially in a context of underdeveloped financial markets, such volatility reduces the level of international trade, affects investment decisions, and hinders growth possibilities. Second, such welfare costs are magnified in the case of prolonged and sustained exchange-rate misalignments, which can badly distort resource allocation.
4. From a policy standpoint, it is therefore critical both to understand the main determinants of the RER, and to distinguish between short- and long-term RER movements. Macroeconomic policies can then be used to smooth “excessive” short-term changes.
5. An accurate analysis of the RER is particularly critical in resource-dependent and in transition economies. Such economies often experience large shocks to fundamentals, such as the terms of trade and productivity, and as a consequence very unstable domestic policies. As a result, their RER may be particularly prone to excess volatility.
6. This paper uses empirical evidence both from Russia’s experience since the beginning of transition, and from the experience of more advanced transition economies, to estimate the determinants of Russia’s RER, and its likely movements over the medium-term. The analysis focuses on estimating the equilibrium RER, that is, the level of the RER consistent with a viable external position. Section B describes the methodology employed to analyze Russia’s historical experience. Section C discusses and describes the available data, including in particular different measures of the RER. Section D reviews the econometric results, and derives the equilibrium RER. Section E concludes.
B. Analyzing Russia’s Historical Experience: Methodology
7. We start by analyzing Russia’s historical experience, following closely the methodology of Edwards (1994) and Mongardini (1998). In order to estimate the equilibrium real exchange rate (ERER), we construct a small structural model tailored to the Russian economy. The key idea is to decompose changes in the actual RER into permanent, fundamental changes, reflecting shocks to the ERER, and more transitory variations, reflecting inter alia shocks to monetary policy. So as to separate these short- and long-term factors, we will estimate a reduced-form equation in an error-correction form.
8. More formally, the structural equation for the ERER is:
where e* is the ERER, and FUND is the vector of fundamental variables, which in particular includes:
The world price of Russian Urals oil. We expect an improvement in the external terms of trade to act to increase the current account balance, and hence appreciate the ERER. Since a terms-of-trade series for Russia is not available, we use as a proxy the price of Russian crude oil. This reflects the fact that exports of hydrocarbons, including oil, oil products, and natural gas, account for a substantial fraction of total Russian exports (over one-half in both 2000 and 2001). Further, oil accounts for most of the hydrocarbon exports, and the prices of other hydrocarbons seem to fluctuate in parallel with oil prices.2
Industrial productivity in Russia, relative to its trading partners. Following the literature on the Balassa-Samuelson effect, we expect increased relative productivity in the economy, particularly in the tradable sector, to lead to an appreciation of the ERER. We focus on industry, as a proxy for tradables, and examine its average labor productivity. In principle, we would like to use total factor productivity, but are unable to make the adjustment because data on the capital stock, capacity utilization, and labor quality are too unreliable and only available for a very limited sample.
Post-1998-crisis structural break dummy. We expect a negative coefficient on this dummy, reflecting two factors. First, any pre-crisis RER overvaluation. Second, the post-crisis RER undershooting.
9. In the short-run, we also assume that the actual RER adjusts towards the equilibrium at a speed given by the parameter β, but that changes in other variables, including in particular policy variables, may disturb the adjustment. Formally,
where e is the actual RER, TEMP is a vector of variables having a transitory effect on the RER, and TEMP* is the vector of such variables that is consistent with the ERER. When specifying TEMP, we focus on the following policy variables:
The excess growth in net international reserves. Specifically, we use the increase in the reserve cover, that is, the ratio of reserves to monthly imports. This reflects the argument that the build up of international reserves since the crisis reflects the authorities’ effort to slow the pace of real appreciation by intervening in the foreign exchange market.
The excess supply of domestic credit, that is, the increase in domestic credit that is unmatched by higher growth in the economy. Specifically, we use the increase in the ratio of ruble broad money to GDP. This is also an attempt to capture shocks to monetary and exchange rate policy.
The change in the fiscal position, relative to lagged high-powered money. A fiscal loosening may have (at least) two opposite effects. First, it may lead to increased spending on nontradables, raising their prices and putting upward pressure on the RER. Second, it may reduce confidence in the sustainability of fiscal, monetary, and exchange rate policy, provoking a reduction in capital inflows and a real depreciation.3
10. Since theory does not restrict the precise short-run dynamics, we use standard information criteria to determine the lag length for these policy variables, and also include in TEMP lagged values of the various fundamental variables.4
11. Following an estimation of the above model, we performed a variance decomposition so as to study the sources of RER variability. In particular, this gave us a measure of how much of the variability in the RER is caused by changes in real variables such as the terms-of-trade and productivity.
C. Data Description
12. We distinguish between two main definitions of the RER. First, we consider the external RER—defined as the nominal exchange rate adjusted for price level differences between countries. This measure compares the relative value of currencies by measuring either the relative prices of foreign and domestic consumption or production baskets, or else the relative input costs. We use the following measures of the external RER: the CPI-based effective RER, and the U.S. dollar value of industrial wages.
13. Second, we consider the internal RER—defined as the ratio of the domestic price of nontradables to tradables within a single country. This concept captures the internal relative price incentive for producing or consuming nontradables as opposed to tradables. As a proxy for nontradables we use paid services. As a proxy for tradables we use food items and other items from the CPI basket.
14. The external and internal measures of the RER show the same qualitative developments between 1995 and today. All measures appreciated sharply from the beginning of 1995 till the 1998 crisis, collapsed in the wake of the crisis, and have again appreciated strongly since then (see Figure 3.1).
Measures of the Real Exchange Rate
(Index, 1995 = 100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Measures of the Real Exchange Rate
(Index, 1995 = 100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Measures of the Real Exchange Rate
(Index, 1995 = 100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
15. Dollar wages, and to a lesser extent the relative price of nontradables, have fluctuated much more widely than the CPI-based real effective exchange rate, owing to a combination of factors. Before the crisis, the much faster increase of dollar wages reflected cost-push pressures against a background of rising competition from imports. The sharp real wage fall during the crisis reflected both nominal wage rigidities, and the fast pass-through from the nominal exchange rate to tradable prices. Finally, the very fast recovery in wages since the crisis is partly a response to their overshooting during the crisis.
16. Still, the difference between the pre-crisis level and the Q3 2002 level is broadly comparable across all three measures (see Table 3.1). The CPI-based REER is 18 percent below its pre-crisis level, while the other two measures are roughly 15 percent below their pre-crisis levels. Since all three measures give the same qualitative picture about developments in competitiveness, and suggest similar changes from pre-crisis levels, we focus in the rest of the paper on the CPI-based REER. However, we check the robustness of the results by also using the other two measures and comparing the results.
Change in Competitiveness Indicators, 1995-2002
(In percent)
Change in Competitiveness Indicators, 1995-2002
(In percent)
| Pre-crisis | Crisis | Difference from the pre-crisis level | |
|---|---|---|---|
| (95 Q1 -98 Q2) | (98Q2-99QI) | Q3 2002 | |
| US dollar value of industrial wages | 117 | -66 | -14 |
| Relative price of nontradables | 96 | -15 | -13 |
| Real effective exchange rate | 57 | -43 | -18 |
Change in Competitiveness Indicators, 1995-2002
(In percent)
| Pre-crisis | Crisis | Difference from the pre-crisis level | |
|---|---|---|---|
| (95 Q1 -98 Q2) | (98Q2-99QI) | Q3 2002 | |
| US dollar value of industrial wages | 117 | -66 | -14 |
| Relative price of nontradables | 96 | -15 | -13 |
| Real effective exchange rate | 57 | -43 | -18 |
17. Regarding links between the RER and other variables, we note that the RER and oil prices seem to have moved roughly together since 1994, although with lags and by different magnitudes (see Figure 3.2). For instance, the oil price increase during 1994-96 was followed by a real appreciation, and the sharp decline between 1997 and the first quarter of 1998 was followed by a real depreciation of more than 40 percent after August 1998. Finally, after the crisis, the sharp pickup in oil prices has been accompanied by a significant real appreciation.
Urals Oil Prices and Real Effective Exchange Rate
(Index, 1995=100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Urals Oil Prices and Real Effective Exchange Rate
(Index, 1995=100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Urals Oil Prices and Real Effective Exchange Rate
(Index, 1995=100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
18. Turning to productivity, before the crisis a steady if relatively slow productivity growth rate was accompanied by a steady real appreciation. Likewise, after the crisis, fast productivity growth was accompanied by a fast real appreciation. However, the RER collapse during the crisis far exceeded any movement in productivity (see Figure 3.3).
Relative Productivity and Real Effective Exchange Rate
(Index, 1995=100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Relative Productivity and Real Effective Exchange Rate
(Index, 1995=100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Relative Productivity and Real Effective Exchange Rate
(Index, 1995=100)
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
19. Overall, this suggests the empirical analysis will face two challenges. First, explaining the timing and magnitude of the 1998 RER collapse; this may only be achievable by using a crisis or structural break dummy. Second, accounting for why the post-crisis appreciation was not faster than observed. One option, discussed earlier, is to consider reserve accumulation. As seen in Figure 3.4, the post-crisis increase in oil prices was accompanied by a rapid increase in international reserves, which could plausibly have dampened the pressure for nominal and perhaps real appreciation.
Urals Oil Prices, Reserve Accumulation, and Real and Nomina Exchange Rates
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Urals Oil Prices, Reserve Accumulation, and Real and Nomina Exchange Rates
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
Urals Oil Prices, Reserve Accumulation, and Real and Nomina Exchange Rates
Citation: IMF Staff Country Reports 2003, 146; 10.5089/9781451833065.002.A003
D. Estimation and Results
20. We now present the results of estimating the model outlined in Section B, and compare the results of our study with those of other studies for transition economies and commodity-exporting countries. We must emphasize that, given the significant uncertainties involved in specifying and estimating this sort of model, particularly in the context of a transition economy, we engage in a broad specification search, and in particular drop several variables that prove to have the wrong sign, This should be borne in mind when examining the reported standard errors.
21. To estimate the long-run co-integrating relationship, we use the Phillips-Loretan single equation error correction mechanism.5 This procedure is appealing in that it offers both a direct way to test the underlying economic theory and simultaneous estimation of the short- and long-run coefficients. However, the procedure does require all variables to have the same order of integration. Therefore, we first check the order of integration of the real exchange rate, oil prices and productivity using the Dickey-Fuller and Phillips-Perron tests. The results (see Table 3.2 below) suggest that we cannot reject the null that all three variables are integrated of order one (I(1)). Of course, such tests have to be treated with caution since in short samples they have extremely low power against economically relevant alternatives.
Unit Root Tests
Unit Root Tests
| Null Hypothesis: URALS has a unit root | |||
| Exogenous: None | |||
| Lag Length: 2 (Automatic based on SIC, MAXLAG=9) | |||
| t-Statistic | Prob.* | ||
| Augmented Dickey-Fuller test statistic | -0.146803 | 0.6261 | |
| Test critical values: | 1 % level | -2.630762 | |
| 5% level | -1.950394 | ||
| Null Hypothesis: URALS has a unit root | |||
| Exogenous: Constant | |||
| Bandwidth: 4 (Newey-West using Bartlett kernel) | |||
| Adj. t-Stat | Prob.* | ||
| Phillips-Person test statistic | -1.935180 | 0.3131 | |
| Test critical values: | 1 % level | -3.626784 | |
| 5% level | -2.945842 | ||
| *MacKinnon (1996) one-sided p-values. | |||
| Null Hypothesis: PRODUCTIVITY has a unit root | |||
| Exogenous: Constant | |||
| Lag Lenqth: 2 (Automatic based on SIC, MAXLAG=9) | |||
| t-Statistic | Prob* | ||
| Augmented Dickey-Fuller test statistic | 0.607692 | 0.9876 | |
| Test critical values: | 1% level | -3.670170 | |
| 5% level | -2.963972 | ||
| Null Hypothesis: PRODUCTIVITY has a unit root | |||
| Exogenous: Constant | |||
| Bandwidth: 31 (Newey-West using Bartlett kernel) | |||
| Adj. t-Stat | Prob.* | ||
| Philips-Person test statistic | 0.782416 | 0.9921 | |
| Test critical values: | 1 % level | -3.653730 | |
| 5% level | -2.957110 | ||
| Null Hypothesis: Real effective exchange rate has a unit root | |||
| Exogenous: Constant | |||
| Lag Lenqth;2 (Automatic based on SIC, MAXLAG=9) | |||
| t-Statistic | Prob.* | ||
| Augmented Dickey-Fuller test statistic | -2.263522 | 0.1897 | |
| Test critical values: | 1 % level | -3.670170 | |
| 5% level | -2.963972 | ||
| Null Hypothesis: Real effective exchange rate has a unit root | |||
| Exogenous: Constant | |||
| Bandwidth: 1 (Newey-West using Bartlett kernel) | |||
| Adj. t-Stat | Prob* | ||
| Phillips-Person test statistic | -1.773892 | 0.3859 | |
| Test critical values: | 1 % level | -3.661661 | |
| 5% level | -2.960411 | ||
Unit Root Tests
| Null Hypothesis: URALS has a unit root | |||
| Exogenous: None | |||
| Lag Length: 2 (Automatic based on SIC, MAXLAG=9) | |||
| t-Statistic | Prob.* | ||
| Augmented Dickey-Fuller test statistic | -0.146803 | 0.6261 | |
| Test critical values: | 1 % level | -2.630762 | |
| 5% level | -1.950394 | ||
| Null Hypothesis: URALS has a unit root | |||
| Exogenous: Constant | |||
| Bandwidth: 4 (Newey-West using Bartlett kernel) | |||
| Adj. t-Stat | Prob.* | ||
| Phillips-Person test statistic | -1.935180 | 0.3131 | |
| Test critical values: | 1 % level | -3.626784 | |
| 5% level | -2.945842 | ||
| *MacKinnon (1996) one-sided p-values. | |||
| Null Hypothesis: PRODUCTIVITY has a unit root | |||
| Exogenous: Constant | |||
| Lag Lenqth: 2 (Automatic based on SIC, MAXLAG=9) | |||
| t-Statistic | Prob* | ||
| Augmented Dickey-Fuller test statistic | 0.607692 | 0.9876 | |
| Test critical values: | 1% level | -3.670170 | |
| 5% level | -2.963972 | ||
| Null Hypothesis: PRODUCTIVITY has a unit root | |||
| Exogenous: Constant | |||
| Bandwidth: 31 (Newey-West using Bartlett kernel) | |||
| Adj. t-Stat | Prob.* | ||
| Philips-Person test statistic | 0.782416 | 0.9921 | |
| Test critical values: | 1 % level | -3.653730 | |
| 5% level | -2.957110 | ||
| Null Hypothesis: Real effective exchange rate has a unit root | |||
| Exogenous: Constant | |||
| Lag Lenqth;2 (Automatic based on SIC, MAXLAG=9) | |||
| t-Statistic | Prob.* | ||
| Augmented Dickey-Fuller test statistic | -2.263522 | 0.1897 | |
| Test critical values: | 1 % level | -3.670170 | |
| 5% level | -2.963972 | ||
| Null Hypothesis: Real effective exchange rate has a unit root | |||
| Exogenous: Constant | |||
| Bandwidth: 1 (Newey-West using Bartlett kernel) | |||
| Adj. t-Stat | Prob* | ||
| Phillips-Person test statistic | -1.773892 | 0.3859 | |
| Test critical values: | 1 % level | -3.661661 | |
| 5% level | -2.960411 | ||
22. In line with the methodology described above, we decompose the fitted RER into the ERER and into transitory factors. The latter include the short-term effect of monetary, exchange rate, and fiscal policy. In contrast, the ERER only incorporates the impact of changes in productivity and Urals oil prices, as well as the structural break dummy.
23. In order to understand what is driving the estimation results, it is useful to start by discussing the estimated long-run cointegrating relationship between the ERER and its underlying determinants, shown in Table 3.3 below. The estimated long-run coefficients have the anticipated signs and are statistically significant. In particular, the long-run elasticity with respect to productivity is approximately 1.3, which is higher than typically found in other studies. The long-run elasticity with respect to oil prices is 0.31, which implies a RER elasticity with respect to the overall terms of trade of about 0.8, well within the range typically reported.
Long-Run Cointegrating Relationship 1/
Standard errors in brackets
Long-Run Cointegrating Relationship 1/
| Post-crisis | ||||
|---|---|---|---|---|
| Constant | Productivity | Urals oil prices | structural break | |
| Phillips-Loretan single equation ECM | 3.30 | 1.30 | 0.31 | -0.62 |
| (0.53) | -(0.46) | (0.12) | (0.13) |
Standard errors in brackets
Long-Run Cointegrating Relationship 1/
| Post-crisis | ||||
|---|---|---|---|---|
| Constant | Productivity | Urals oil prices | structural break | |
| Phillips-Loretan single equation ECM | 3.30 | 1.30 | 0.31 | -0.62 |
| (0.53) | -(0.46) | (0.12) | (0.13) |
Standard errors in brackets
24. We suspect these estimates may be distorted by three problems. First, oil prices are not a perfect proxy for the terms of trade. Second, our chosen policy variables, discussed below, may represent only part of the mechanism through which the response of the RER to oil prices in the post-crisis period was muted. Third, and linked, it may be hard to disentangle the separate effects of productivity and oil prices, given that they both grew strongly after the crisis. Overall, the result of these problems may well be that the coefficient on oil prices is biased downwards, and possibly that some of the impact of oil prices is wrongly attributed to productivity.
25. Subject to these caveats, and as expected, both productivity and oil prices clearly play an important role in explaining movements of the ERER over extended periods. To quantify the economic significance of these factors, we now compute the contribution of productivity and of oil prices to the ERER appreciation between end-1998 and end-2001. The estimated long-run coefficients imply that productivity accounts for roughly 60 percent, and oil prices for 40 percent, of the appreciation of the ERER.
26. One should be extremely cautious in interpreting these results, because the structural break dummy is statistically extremely significant, and also very large. The estimated hemi-elasticity of around 0.60 implies an almost 50 percent drop in the fitted RER immediately after the crisis, even after controlling for all observable fundamentals. To the extent that the dummy reflects (at least partly) post-crisis RER undershooting, rather than pre-crisis overvaluation, this sharp drop is likely to be corrected in the future.6 The critical problem is that, in the absence of forward-looking information, it is hard to gauge what weight to attach to different interpretations of the dummy.
27. Since the beginning of 1999, the overall fitted RER appreciated less than the ERER, reflecting two factors. First, monetary policy, as proxied by the share of reserves in imports, was somewhat successful in the short-term in slowing real appreciation. Second, the RER only adjusts with some lag towards its equilibrium level, likely reflecting real rigidities in the economy. The estimated speed-of-adjustment coefficient implies that, after two quarters, about 70 percent of a shock to the RER has been absorbed.7 At this stage, we also note that the estimated coefficients on both excess credit and fiscal policy are insignificant. The former finding suggests that foreign exchange intervention (even if sterilized) may successfully retard real appreciation; the latter finding may not be surprising in view of the offsetting effects of the considerable tightening of fiscal policy, as discussed earlier.
28. Regarding the impact of long-run productivity growth, our results suggest the presence of a strong Balassa-Samuelson effect. The significance of this lies in the fact that such an effect should not be resisted by monetary policy. Over the last three years, average whole economy labor productivity growth in Russia was approximately 4.5 percent, implying a 2-3 percentage point productivity growth differential vis-à-vis the OECD. Assuming that as a result of structural reforms the productivity growth differential will remain 2-3 percentage points, in the long-run the ERER will also continue growing by perhaps 2-3 percent per annum (since the coefficient linking the ERER to relative productivity is close to unity).
29. Finally, the estimation results remain broadly stable, or indeed improve in significance, if one uses alternative measures of the RER. For instance, replacing the CPI-based REER with U.S. dollar wages still implies a large, significant ERER elasticity with respect to productivity, and it raises the significance level of the estimated coefficients on oil prices and on monetary policy.
30. The methodology employed so far, while extremely popular, relies on two crucial assumptions. First, that a stable relationship between the RER and its underlying determinants can be gleaned from historical data. Second, that this relationship will continue to hold in the future. In the case of Russia, where the transition process is still very much underway, and given the clear evidence for an in-sample structural break, such assumptions need to be treated cautiously. In particular, the methodology cannot be reliably used to discuss the current or future level of the ERER.
31. Therefore, an avenue for future work would be to test the plausibility of the results discussed above by applying alternative methods. One alternative is Williamson’s “fundamental equilibrium exchange rate” approach (extended in Isard and Faruqee (1998) and Isard et al. (2001)). A comparison of the outcomes from this method with the results from the econometric approach would ensure a better assessment of the degree of under/overvaluation the real exchange rate with respect to its long-run equilibrium level.
E. Conclusions
32. Any analysis of the equilibrium RER is subject to large uncertainties. However, empirical analysis confirms a link between the ERER and productivity, in both Russia and other transition economies. Dependence of the Russian ERER on oil prices can also be observed, in spite of the authorities’ attempts to offset such links through monetary policy and foreign reserve accumulation; the dependence is likely to become even clearer over the long run.
33. Over the long-run, and even after any RER disequilibrium is resolved, the link between the ERER and productivity implies that we should expect both the actual and the equilibrium RER to continue appreciating. The precise rate of productivity-driven appreciation will depend on the speed and determination with which structural reforms are pursued; on average, it has equaled about 2-3 percent per annum in other transition economies.
34. To the extent that the authorities resist nominal appreciation, the real appreciation discussed above would have to occur through increases in domestic prices, particularly of nontradables, and would also show up as higher CPI inflation. It should be emphasized that such appreciation, whether reflecting an adjustment to the current disequilibrium, or the response to continuing productivity growth, should not be viewed as a threat to growth, employment, or external sustainability. Rather, it represents a mechanism which ensures that the Russian people will achieve a standard of living commensurate with their productivity and external environment. The paper did not tackle the question of the optimal speed of RER adjustment, but we would like to finish by emphasizing the standard conclusion that such adjustment could proceed relatively rapidly, so long as (a) structural reforms are pursued diligently, so as to reduce the extent of structural rigidities in the economy; and (b) output, employment rates, and capacity utilizations do not show signs of precipitous declines.
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Prepared by Nikola Spatafora (RES) and Emil Stavrev (EU2).
We tried constructing an aggregate price index for Russian energy exports, using a weighted average of Russian oil and natural gas prices, but this variable failed to improve on the simple Urals oil price.
We tried including also a debt-stock variable, to capture separately this confidence effect, but received unsatisfactory results. Also, we tried allowing our fiscal variable to affect the ERER too, but received counter-intuitive results.
We would have liked to include a measure of net foreign assets (NFA) among the fundamental variables. However, the Lane and Milesi-Ferretti (2001) NFA dataset does not include Russia. We did try including the measure of NFA reported in IFS, but this did not work satisfactorily, likely because of the various problems discussed by Lane and Milesi-Ferretti.
For details, see Phillips and Loretan (1991).
We tried including a pure “August 1998 crisis dummy,” but this was not significant after controlling for the structural break.
The speed of adjustment (the coefficient on the disequilibrium term) and the elasticity with respect to monetary policy are given in the following equation, which shows the full dynamics:
where Reserves is gross reserves in dollars, Imports is nominal dollar imports, Urals is Urals oil price, and numbers in parentheses denote standard errors.
