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Nikolay Gueorguiev is an Economist in the IMF’s European I Department. The author would like to thank Leo Bonato, Nicolas Carnot, Neven Mates, Emil Stavrev, and seminar participants at IMF headquarters for helpful comments and suggestions.
McCarthy (2000) augments this model with a central bank reaction function (based on using the interest rate as an instrument) and a money demand equation. In our case, including these variables left too few degrees of freedom, which led to implausible estimation results.
Owing to considerable increases in administered prices, the share of the respective goods and services in the CPI increased from 13 percent in 1999 to 23 percent in 2002. Since these prices are determined by administrative decision, only loosely related to exchange rate developments, we felt that their inclusion would obscure the “real” pass-through, defined as the impact of an exchange rate shock to market-determined prices.
In addition, as the purpose of this paper is to provide input for monetary policy decisions, we felt that these were the most useful benchmarks.
Strictly speaking, the VAR system should be represented as a vector error-correction model, including possible error correction terms stemming from potential cointegration between some of the included variables in levels. However, cointegration tests indicate too many cointegration relationships between the six included variables, with low (and often borderline significant at best) adjustment coefficients. Following the rest of the literature, we omit the error correction terms.
While the share of trade flows in U.S. dollars is estimated at about 1/3 of total trade, it is disproportionately high in imports of intermediate inputs, exceeding 50 percent. See the top panel of Table A5. Intermediate inputs constitute over 60 percent of the overall Romanian imports.
The model including the Lei/U.S. dollar exchange rate appears the best on statistical grounds as well. Starting with a general specification, including both the Lei/U.S. dollar exchange rate and the Lei/Euro one and testing the exclusion of each of them, a likelihood ratio test was able to exclude the latter, but not the former. In addition, impulse responses from that model are significantly different from zero (based both on asymptotic and Monte Carlo standard errors with 1000 repetitions) up to the fourth period ahead for both PPI and CPIC inflation, while the relevant impulse responses from the two alternative models were generally not significantly different from zero.
The size of this sample allows the use of up to two lags. Fortunately, this is not a problem, as one lags is sufficient to remove autocorrelation from the estimated residuals. Still, these results should be viewed with more caution than the ones from the full sample, as the fewer degrees of freedom can affect the efficiency of the estimation.
This effect comes from the impact of the estimated mean of exchange rate changes on the mean of producer and consumer price inflation in the infinite-moving-average representation of the VAR and can be interpreted as “expected” contribution, in the absence of unforeseen shocks.
This is the sum of the products of the first 18 impulse responses and their corresponding structural shocks.
In a high-inflation environment, even price setting of nontradables reflects expectations about future exchange rate dynamics as a proxy for expected inflation. So the importance of the Lei/U.S. dollar exchange rate may also reflect its role as a benchmark targeted by the monetary authority throughout most of the analyzed period. One could argue that now that this role has been taken by the 60/40 basket, and will later be taken by the euro, price changes will become more sensitive to changes in the Lei/Euro exchange rate than before. An offsetting argument is that at current lower levels of inflation (and especially when it falls to single digits), the signaling role of the exchange rate for future inflation declines and therefore this channel of pass-through decreases in significance.
I am indebted to Mrs. Surica Rosentuller, head of the Research Department of the National Bank of Romania for this data series.