A reappraisal of the Meese–Rogoff puzzle

Abstract

Several explanations have been put forward for the Meese–Rogoff puzzle that exchange rate models cannot outperform the random walk in out-of-sample forecasting. We suggest that a simple explanation for the puzzle is the use of the root mean square error (RMSE) to measure forecasting accuracy, presenting a rationale as to why it is difficult to beat the random walk in terms of the RMSE. By using exactly the same exchange rates, time periods and estimation methods as those of Meese and Rogoff, we find that their results cannot be overturned even if the models are estimated with time-varying coefficients. However, we also find that the random walk can be outperformed by the same models if forecasting accuracy is measured in terms of the ability to predict direction, in terms of a measure that combines magnitude and direction and in terms of profitability.