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The Use of Predictive Regressions at Alternative Horizons in Finance and Economics

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Author Info

  • Nelson C. Mark

    (University of Notre Dame)

  • Donggyu Sul

    (University of Auckland)

Abstract

When a k period future return is regressed on a current variable such as the log dividend yield, the marginal significance level of the t-test that the return is un- predictable typically increases over some range of future return horizons, k. Local asymptotic power analysis shows that the power of the long-horizon predictive regression test dominates that of the short-horizon test over a nontrivial region of the admissible parameter space. In practice, small sample OLS bias, which differs under the null and the alternative, can distort the size and reduce the power gains of long-horizon tests. To overcome these problems, we suggest a moving block recursive Jackknife estimator of the predictive regression slope coefficient and test statistics that is appropriate under both the null and the alternative. The methods are applied to testing whether future stock returns are predictable. Consistent evidence in favor of return predictability shows up at the 5 year horizon.

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File URL: http://128.118.178.162/eps/fin/papers/0409/0409032.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Finance with number 0409032.

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Length: 39 pages
Date of creation: 08 Sep 2004
Date of revision:
Handle: RePEc:wpa:wuwpfi:0409032

Note: Type of Document - pdf; pages: 39
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Web page: http://128.118.178.162

Related research

Keywords: Predictive regression; Long horizons; Stock returns; Small sample bias; Local asymptotic power;

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References

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Citations

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Cited by:
  1. Hjalmarsson, Erik, 2005. "On the Predictability of Global Stock Returns," Working Papers in Economics 161, University of Gothenburg, Department of Economics.
  2. Erik Hjalmarsson, 2006. "New methods for inference in long-run predictive regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.).
  3. Kim, Young Se, 2009. "Exchange rates and fundamentals under adaptive learning," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 843-863, April.

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