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The authors are grateful to Jacob Frenkel, Sterie Beza, Charles Adams, and the IMF desk economists for Argentina, Bolivia, Brazil, Chile, and Peru for constructive comments on a previous version of this paper, although responsibility for any remaining errors or omissions rests with the authors. Any views expressed are those of the authors and are not necessarily those of the International Monetary Fund.
More sophisticated analyses (e.g., Montiel, 1989; Calomiris and Domowitz, 1989) are clearly important in understanding the macroeconomics of these countries in greater detail. Our objective, however, is to capture the main features of the data with a very simple model and carefully applied econometrics.
Taylor (1991) investigates the plausibility of the assumption of stationary forecasting errors [when the variable being forecast is I(1)] for a range of alternative assumptions concerning expectations formation.
The following analysis can allow forecasting errors to be serially correlated and heteroskedastic. This would be the case if, for example, expectations were adaptive, which would imply moving average errors whose conditional variance was also serially correlated (Engle, 1982). Phillips (1987) suggests that the Johansen maximum likelihood technique, which is applied below, is applicable in the presence of heterogeneously distributed error processes. Thus, the properties required of the errors are very weak (see the conditions imposed on the error processes in the analysis in Phillips, 1987).
Note that we only require ϵt to be non-stationary, not serially uncorrelated. ϵt may be serially correlated because of omitted variables (ψt) and/or because expectational errors are serially correlated.
Under the additional assumption of rational expectations, this implication of the hyperinflation model is a particular case of a general result for present value models discussed by Campbell and Shiller (1987). A main purpose of the present analysis, however, is to derive a test of the hyperinflation model which is nonspecific with respect to expectations formation. The importance of considering alternative forms of expectations formation in the context of testing present value models is demonstrated by Chow (1989). Rather than use least squares, however, in the empirical work reported below, we apply a maximum likelihood technique for estimating α due to Johansen (1988), since this technique has the added advantage of allowing tests of linear restrictions on the cointegrating parameters to be easily constructed.
Melnick (1990) estimates a real money demand function for Argentina, 1978-1985, with inflation and output as explanatory variables. Melnick finds, however, that the I(1) hypothesis can only be marginally rejected (a Dickey-Fuller statistic of -2.83 against a 5 percent critical value of -2.89 (Fuller, 1976)). Note, however, that Melnick erroneously quotes the critical values from Engle and Granger (1987) rather than Fuller (1976).
Named for the Finance Minister, Luis Bresser Pereira, who replaced the originator of the Cruzado plan, Dilson Funaro.
See IMF Survey, January 26, 1987.
Exact sources are: money--IFS, line 34, prices--IFS, line 64.
As a check on the robustness of these results, we performed the same unit root tests with the sample period in each case truncated at December 1980 (results not reported). In nearly every case, the results were qualitatively identical to those reported above--i.e., both inflation and real balances appear to be I(1). The sole exception is for Bolivian inflation 1971:1-1980:12, for which the I(1) hypothesis is rejected at the 5 percent level. In the remainder of the paper, however, we continue to assume that Bolivian inflation was generated according to an I(1) process over the whole sample on two grounds. First, it is possible that although a unit root is present in the data up to 1980, there is insufficient variability in the 1970s data to detect its presence. Secondly, if we consider the testing process as a whole--we examine two series for five countries for both full and sub-samples--the probability of committing at least one type I error (in this case rejection of the I(1) hypothesis when it is in fact correct for the Bolivian sub-sample) will be high. In any case, using the Bolivian data from January 1981 onwards yielded results qualitatively identical to those reported in the remainder of the paper.
The parameter α can be estimated by cointegration analysis and, because of the super-consistency of such an estimate when the variables are cointegrated (Stock, 1987), treated as known in testing (5).
Taylor (1990) demonstrates that this is equivalent to testing a set of cross-equation rational expectations restrictions on the vector autoregressive representation of [Δ2pt,(m-p)t-αΔpt)]’.
Data for the U.S. consumer price index was taken from the International Financial statistics data tape (IFS, line 64) and for the black market exchange rates from Pick’s World Currency Yearbook, various issues, with missing values kindly supplied by the IMF desk economists for the relevant countries.