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Inference in Long-Horizon Regressions

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  • Hjalmarsson, Erik

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.

Suggested Citation

  • Hjalmarsson, Erik, 2006. "Inference in Long-Horizon Regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.), revised Oct 2008.
  • Handle: RePEc:fip:fedgif:853
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    References listed on IDEAS

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    Cited by:

    1. Qiu, Mei & Pinfold, John F. & Rose, Lawrence C., 2011. "Predicting foreign exchange movements using historic deviations from PPP," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 485-497, October.

    More about this item

    Keywords

    Predictive regressions; Long-horizon regressions; Stock return predictability;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets

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