Dynamic Forecasting of Monetary Exchange Rate Models: Evidence from Cointegration

Article excerpt


The Frenkel-Bilson and Dornbusch-Frankel monetary exchange rate models are used to estimate the out-of-sample forecasting performance for the U.S. dollar/Canadian dollar exchange rate. By using Johansen's multivariate cointegration, up to three cointegrating vectors were found between the exchange rate and macroeconomic fundamentals. This means that there is a long-run relationship between the exchange rate and economic fundamentals. Based on error correction models, two monetary models outperform the random walk model at the three-, six-, and 12-month forecasting horizons. Therefore, monetary exchange rate models are still useful in forecasting exchange rates. (JEL F31)


It is long believed that nominal exchange rate behavior is well described by the naive random walk model. This means that there are no systematic economic forces in determining exchange rates. Meese and Rogoff [1983] show that none of the structural models (Frenkel-Bilson's flexible-price monetary model, Dornbusch-Frankel's sticky-price monetary model, Hooper-Morton's [1982] sticky-price asset model) outperform a simple random walk on the basis of the root mean squared error (RMSE) and mean absolute error criteria for forecast evaluation. The poor empirical performance of these structural exchange rate models could be the result of simultaneous equation bias, sampling error, stochastic movement in the true underlying parameters, and misspecification of the underlying models. [1]

However, not all writers present results that reject structural exchange rate models. Woo [1985] incorporates a money demand function with a partial adjustment mechanism and finds that a reformulated monetary approach can outperform the random walk model in an out-of-sample forecast exercise. Somanath [1986] also finds that a monetary model with a lagged endogenous variable forecasts better than the naive random walk model. Finn [1986] argues that the simple flexible-price monetary model is not supported by the data while the rational-expectations monetary model is supported and performs as well as the random walk model.

MacDonald and Taylor [1993, 1994] also claim some predictive power for the monetary model. MacDonald and Taylor [1993] examine the monetary model of the exchange rate between the German mark and the U.S. dollar over the period January 1976 to December 1990. They find that a dynamic error correction model outperforms. the random walk forecast at every forecast horizon. Using a multivariate cointegration technique, MacDonald and Taylor [1994] also find that an unrestricted monetary model outperforms the random walk and other models in an out-of-sample forecasting experiment for the sterling-dollar exchange rate.

Mark [1995] presents evidence that long-term horizon changes in log nominal exchange rates are predictable using the U.S. dollar price of the Canadian dollar, the German mark, the Swiss franc, and the Japanese yen from 1973:2 to 1991:4. For three out of four exchange rates, the out-of-sample forecasts of the regression outperform the driftless random walk model at the 12- and 16-quarter horizons.

Using both parametric and nonparametric estimation techniques, Chinn and Meese [1995] examine the forecasting performance of three structural exchange rate models for bilateral exchange rates (Canada, Germany, Japan, and the United Kingdom), relative to the U.S. dollar, over March 1973 to December 1990. They showed that three structural exchange rate models cannot predict better than a random walk model for short-term horizons. However, for long-term horizons (36 months), these structural models show more predictive power than the random walk model.

Goldberg and Frydman [1996] found that all structural exchange rate models considered outperformed the random walk model in the out-of-sample forecast within the separate regimes of stability for the U.S. dollar/German mark exchange rate from March 1973 to March 1988. …