Exchange rates and Fundamentals: What Do We Learn From Long-Horizon Regressions?
AbstractLong-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.
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Bibliographic InfoPaper provided by Research Seminar in International Economics, University of Michigan in its series Working Papers with number 401.
Length: 31 pages
Date of creation: 1997
Date of revision:
REGRESSION ANALYSIS ; FINANCE ; EXCHANGE RATE ; FORECASTS;
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