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Hypothesis Testing in Predictive Regressions

Author

Listed:
  • Yakov Amihud

    (New York University)

  • Clifford Hurvich

    (New York University)

  • Yi Wang

    (New York University)

Abstract

We propose a new hypothesis testing method for multi-predictor regressions with finite samples, where the dependent variable is regressed on lagged variables that are autoregressive. It is based on the augmented regressiom method (ARM; Amihud and Hurvich (2004)), which produces reduced-bias coefficients and is easy to implement. The method's usefulness is demonstrated by simulations and by an empirical example, where stock returns are predicted by dividend yield and by bond yield spread. For single-predictor regressions, we show that the ARM outperforms bootstrapping and that the ARM performs better than Lewellen's (2003) method in many situations.

Suggested Citation

  • Yakov Amihud & Clifford Hurvich & Yi Wang, 2004. "Hypothesis Testing in Predictive Regressions," Finance 0412022, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:0412022
    Note: Type of Document - pdf; pages: 47
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    References listed on IDEAS

    as
    1. Amihud, Yakov & Hurvich, Clifford M., 2004. "Predictive Regressions: A Reduced-Bias Estimation Method," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 39(4), pages 813-841, December.
    2. Maynard, Alex & Shimotsu, Katsumi, 2009. "Covariance-Based Orthogonality Tests For Regressors With Unknown Persistence," Econometric Theory, Cambridge University Press, vol. 25(1), pages 63-116, February.
    3. Baker, Malcolm & Stein, Jeremy C., 2004. "Market liquidity as a sentiment indicator," Journal of Financial Markets, Elsevier, vol. 7(3), pages 271-299, June.
    4. Kothari, S. P. & Shanken, Jay, 1997. "Book-to-market, dividend yield, and expected market returns: A time-series analysis," Journal of Financial Economics, Elsevier, vol. 44(2), pages 169-203, May.
    5. Polk, Christopher & Thompson, Samuel & Vuolteenaho, Tuomo, 2006. "Cross-sectional forecasts of the equity premium," Journal of Financial Economics, Elsevier, vol. 81(1), pages 101-141, July.
    6. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    7. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    8. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    9. Lewellen, Jonathan, 2003. "Predicting Returns With Financial Ratios," Working papers 4374-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    10. Keim, Donald B. & Stambaugh, Robert F., 1986. "Predicting returns in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 17(2), pages 357-390, December.
    11. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    12. Nelson, Charles R & Kim, Myung J, 1993. "Predictable Stock Returns: The Role of Small Sample Bias," Journal of Finance, American Finance Association, vol. 48(2), pages 641-661, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Martin Lettau & Stijn Van Nieuwerburgh, 2008. "Reconciling the Return Predictability Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1607-1652, July.
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    3. Jacob Boudoukh & Matthew Richardson & Robert Whitelaw, 2005. "The Myth of Long-Horizon Predictability," NBER Working Papers 11841, National Bureau of Economic Research, Inc.
    4. Maynard, Alex & Shimotsu, Katsumi, 2009. "Covariance-Based Orthogonality Tests For Regressors With Unknown Persistence," Econometric Theory, Cambridge University Press, vol. 25(1), pages 63-116, February.

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    More about this item

    Keywords

    Augmented Regression Method (ARM); Bootstrapping; Hypothesis Testing;
    All these keywords.

    JEL classification:

    • G - Financial Economics

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