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Multiple-Predictor Regressions: Hypothesis Testing

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  • Yakov Amihud
  • Clifford M. Hurvich
  • Yi Wang
Abstract
We propose a new hypothesis-testing method for multipredictor regressions in small samples, where the dependent variable is regressed on lagged variables that are autoregressive. The new test is based on the augmented regression method (Amihud and Hurvich, 2004), which produces reduced-bias coefficients and is easy to implement. The method's usefulness is demonstrated by simulations and by testing a model where stock returns are predicted by two variables, income-to-consumption and dividend yield. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org, Oxford University Press.

Suggested Citation

  • Yakov Amihud & Clifford M. Hurvich & Yi Wang, 2009. "Multiple-Predictor Regressions: Hypothesis Testing," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 413-434, January.
    • Handle: RePEc:oup:rfinst:v:22:y:2009:i:1:p:413-434
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    File URL: http://hdl.handle.net/10.1093/rfs/hhn056
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