The out-of-sample forecasting performance of exchange rate models when coefficients are allowed to change

This study examines the out-of-sample forecasting performance of models of exchange rate determination without imposing the restriction that coefficients are fixed over time. Both fixed and variable coefficient versions of conventional structural models are considered, with and without a lagged dependent variable. While our results on fixed coefficient models support most of the Meese and Rogoff conclusions, we find that when coefficients are allowed to change, an important subset of conventional models of the dollar-pound, the dollar-deutsche mark, and the dollar-yen exchange rates can outperform forecasts of a random walk model. The structural models considered are the flexible-price (Frenkel-Bilson) and sticky-price (Dornbusch-Frankel) monetary models, and a sticky-price model which includes the current account (Hooper-Morton). We also find that the variable coefficient version of the Dornbusch-Frankel model with a lagged dependent variable generally predicts better than the other models considered including the random walk model.