Exchange rates and Fundamentals: What Do We Learn From Long-Horizon Regressions?

Long-horizon regression tests are widely used in empirical finance, despite evidence of severe size distortions. I propose a new bootstrap method for small-sample inference in long-horizon regressions. A Monte Carlo study shows that this bootstrap test greatly reduces the size distortions of conventional long-horizon regression tests. I also find that long-horizon regression tests do not have power advantages against economically plausible alternatives. The apparent lack of higher power at long horizons suggests that previous findings of increasing long-horizon predictability are more likely due to size distortions than to power gains. I illustrate the use of the bootstrap method by analyzing whether monetary fundamentals help predict changes in four major exchange rates.