Inference in Long-Horizon Regressions

Erik Hjalmarsson ()
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Erik Hjalmarsson: https://www.gu.se/en/about/find-staff/erikhjalmarsson

No 853, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)

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 (search for similar items in EconPapers)
JEL-codes: C22 G1 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2006
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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