Borenzstein, Eduardo, and José de Gregorio, 1999, “Devaluation and Inflation after Currency Crisis” (Washington, D.C.: International Monetary Fund) (draft).
Goldfajn, Ilan, and Sérgio Werlang, 2000, “The Pass-Through from Depreciation to Inflation: A Panel Study.” Working Paper Series (Central Bank of Brazil), No. 5 (July).
McCarthy, Jonathan, 1999, “Pass-Through of Exchange Rates and Import Prices to Domestic Inflation in Some Industrialized Economies,” Bank for International Settlements Working Paper, No. 79.
Nelson, Charles R., and Charles I. Plosser, 1982, “Trends and Random Walks in Macroeconomic Time Series,” Journal of Monetary Economics, Vol. 10, pp. 139–162.
Rabanal, Pau, and Gerd Schwartz, 2000, “Testing the Effectiveness of the Overnight Interest Rate as a Monetary Policy Instrument” (Washington, D.C.: International Monetary Fund) (Section VI in this document).
Schwartz, Gerd, 1999, “Price Developments in Brazil After the Floating of the Real: the First Six Months,” In Brazil—Selected Issues and Statistical Appendix, IMF Staff Country Report No. 99/97, September (Washington, D.C.: International Monetary Fund).
Sims, Chris A., James Stock and Mark Watson, 1990, “Inference in Linear Time Series Models with Some Unit Roots”, Econometrica, Vol. 58, pp. 113–144.
Prepared by Pau Rabanal and Gerd Schwartz.
Passthrough is defined here as the cumulative consumer price inflation relative to the cumulative depreciation of the real vis-à-vis the U.S. dollar.
The series reflects unit values for imports of goods and nonfactor services, as published by FUNCEX, where the original index was transformed using the prevailing exchange rate.
Wholesale prices reflect the wholesale price index (IPA-DI) of the Getulio Vargas Foundation (FGV); an alternative wholesale price index (IPA-OG), also published by FGV, which has a higher component of imported goods than the IPA-DI, delivered very similar results. For a similar comparison, using only the industry component of the IPA-OG index, see IPEA (2000). Consumer price inflation is measured on the basis of the IPCA, published by the IBGE.
While, ideally, the passthrough should be measured using only data for the floating exchange rate regime that started in mid-January 1999, there are only some 20 observations so far. Using some data from the fixed exchange rate period is likely to bias our estimates toward a low passthrough. Therefore, it reduces the measured impact of the large depreciation following the floating of the real in January 1999.
In applying Mc Carthy’s (1999) model to Brazil, output was proxied alternatively by industrial production, and by a series of monthly output proxies, prepared by the BCB. Both series yielded rather similar results, and we decided to use the monthly series of output proxies, as it seemed more comprehensive.
About 90 percent of the components of the WPI are tradables, while the CPI contains about 50 percent of tradables. Initially we considered including wages in the VAR. However, since the sample suggested that the exchange rage did not Granger-cause wages, and that wages did not Granger-cause price indicators, wages were dropped from the VAR.
The Granger causality tests for the subperiod January 1999 to September 2000 only used one lag.
The unit root tests generally suggested to estimate the VAR in first differences. Still, by estimating the VAR in levels, we allow for the possibility of cointegration between variables. Using Johansen’s cointegration test in the e-Views econometrics package, we identified three cointegrating relationships between the five variables in the system. While this test is useful in identifying the number of cointegrationg relationships, it does not offer guidance on which variables are actually cointegrated. However, given the evidence on the presence of cointegration, we assume that estimating the VAR in levels is a valid strategy. See Nelson and Plosser (1982), and Sims et al. (1990) for a discussion on estimating VAR models when series are nonstationary and possibly cointegrated.
The passthrough is measured here by PTt, t+j=Pt, t+j/Et, t+j, where PTt, t+j denotes the cumulative passthrough after j months, Pt, t+j the cumulative change in the price level after j months, and Et, t+j is the cumulative change in the nominal exchange rate after j months. As presented here, the passthrough is measured based on changes in the R$/US$ rate, i.e., a change in the exchange rate from R$l.2 per U.S. dollar to R$1.8 per U.S. dollar implies a depreciation of 50 percent, i.e., (1.8/1.2-1)* 100. As measured by Schwartz (1999), the same change would have implied a depreciation of 33.3 percent, i.e., (1.2/1.8-1)* 100.
For the specification in first differences, monthly responses were accumulated to obtain the cumulative response of the levels of the variables. We estimate the VAR in both cases with three lags of every endogenous variable. Therefore, when we estimate the VAR in first differences, we are effectively using up to four lags of every endogenous variable.