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.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||1997|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://fordschool.umich.edu/rsie/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:mie:wpaper:401. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (FSPP Webmaster)
If references are entirely missing, you can add them using this form.