a b s t r a c t
Exchange rate forecasting is hard and the seminal result of Meese and Rogoff [Meese, R., Rogoff, K., 1983.
Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International
Economics 14, 324] that the exchange rate is well approximated by a driftless random walk, at
least for prediction purposes, still stands despite much effort at constructing other forecasting models.
However, in several other macro and financial forecasting applications, researchers in recent years
have considered methods for forecasting that effectively combine the information in a large number of
time series. In this paper, I apply one such method for pooling forecasts from several different models,
Bayesian Model Averaging, to the problem of pseudo out-of-sample exchange rate predictions. For
most currencyhorizon pairs, the Bayesian Model Averaging forecasts using a sufficiently high degree
of shrinkage, give slightly smaller out-of-sample mean square prediction error than the random walk
benchmark. The forecasts generated by this model averaging methodology are however very close to,
but not identical to, those from the random walk forecast.