Meese-Rogoff Redux: Micro-Based Exchange-Rate Forecasting

摘要

This paper compares the true, ex ante forecasting performance of a micro-based model against both a standard macro model and a random walk. In contrast to existing literature, which is focused on longer-horizon forecasting, we examine forecasting over horizons from one day to one month (the one-month horizon being where micro and macro analysis begin to overlap). Over our three-year forecasting sample, we find that the micro-based model consistently outperforms both the random walk and the macro model. Micro-based forecasts account for almost 16 percent of the sample variance in monthly spot rate changes. These results provide a level of empirical validation as yet unat-tained by other models. The forecasting experiment proposed by Richard Meese and Kenneth Rogoff (1983) remains a benchmark against which exchange-rate models are judged. Their result that structural macro models cannot outperform a naive random walk has proved robust over the decades. Yet, the Meese-Rogoff paper was never about forecasting in the true sense (i.e., using time-t information to forecast exchange rates at t + 1). By using concurrent, realized values of the forcing variables, their regressions were more about concurrent explanation than about ex ante forecasting. Their only "forecasting" element is in their reliance on ex ante data to estimate equation parameters, which appropriately penalized models whose estimated parameters were unstable.