Working Paper
Inference in Long-Horizon Regressions
Abstract: I develop new results for long-horizon predictive regressions with overlapping observations. I show that rather than using auto-correlation robust standard errors, the standard t-statistic can simply be divided by the square root of the forecasting horizon to correct for the effects of the overlap in the data; this is asymptotically an exact correction and not an approximate result. Further, when the regressors are persistent and endogenous, the long-run OLS estimator suffers from the same problems as does the short-run OLS estimator, and it is shown how similar corrections and test procedures as those proposed for the short-run case can also be implemented in the long-run. New results for the power properties of long-horizon tests are also developed. The theoretical results are illustrated with an application to long-run stock-return predictability, where it is shown that once correctly sized tests are used, the evidence of predictability is generally much stronger at short rather than long horizons.
Keywords: Predictive regressions; Long-horizon regressions; Stock return predictability;
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https://www.federalreserve.gov/pubs/ifdp/2006/853/ifdp853r.pdf
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https://www.federalreserve.gov/pubs/ifdp/2006/853/ifdp853.pdf
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Bibliographic Information
Provider: Board of Governors of the Federal Reserve System (U.S.)
Part of Series: International Finance Discussion Papers
Publication Date: 2008-10
Number: 853
Pages: 57 pages