Handbook of Exchange Rates

9.1 Introduction

Out-of-sample forecasts of exchange rates in the late 1990s and 2000s generated by time-series regression models have fared poorly. These forecasts are typically dominated (in mean-square error) by the driftless random walk. On the other hand, pooled regression models estimated on panel data (allowing for fixed effects) have, in many instances, performed much better than forecasts generated by time-series regression models. The superior predictive performance of the pooled panel data models is a puzzle because the evidence also reports of significant underlying model heterogeneity, in which case econometric theory tells us that pooling is inappropriate. Groen (2005), Rapach and Wohar (2004), Basher and Westerlund (2009) address the question about whether or not it is appropriate to pool. In this chapter, we ask when is it (under what conditions) that pooled regression forecasts generate more accurate exchange rate predictions than time-series regressions when the available econometric theory says that you should not pool.

The empirical literature on which we focus traces its origin to, and is motivated toward overturning the findings of Meese and Rogoff (1983), who in studying floating exchange rates in the post Bretton Woods era, demonstrated that the driftless random walk model dominated economic theory-based econometric models (e.g., purchasing-power parity models or simple monetary models) in out-of-sample forecast accuracy. Using time-series regression models, ...