Handbook of Exchange Rates

9.6 Conclusions

The empirical literature finds that out-of-sample exchange rate forecasts over the 1990s and 2000s from (pooled) panel data models often perform better than those generated from time-series regression models. Researchers have reported these findings, even though there is substantial model heterogeneity across countries (or currencies) and asymptotic analysis tells us that pooling should be inappropriate in this case.

What we have shown is that if one is interested in evaluating out-of-sample forecast ability, pooling does not always generate more accurate forecasts than time-series regression. Pooling dominates when there is not much heterogeneity in the model parameters. When the heterogeneity is great, time-series forecasts should be used. However, if one is interested in testing for predictive ability using the full sample, pooling will result in more powerful tests as long as the model parameters and regressors are uncorrelated with the regression errors. Under these conditions, it makes sense to pool whether or not one believes that there is underlying slope heterogeneity.

¹Groen (2000) and Husted and MacDonald (1998) exploit panel data to test restrictions implied by the monetary model and find the evidence for the monetary model to be much stronger using panel data, but these papers do not engage in out-of-sample prediction. Rogoff and Stavrakeva (2008) find that panel data models with time dummies do not produce robust forecasting results.

²As S → ∞ regardless ...