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Estimation And Inference In Predictive Regressions

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  • KUROZUMI, EIJI
  • AONO, KOHEI

Abstract

In this paper, we analyze feasible bias-reduced versions of point estimates for predictive regressions: The plug-in estimates, which are based on the augmented regressions proposed by Amihud and Hurvich (2004) and Amihud, Hurvich and Wang (2010), and the grouped jackknife estimate by Quenouille (1949, 1956).We also derive the correct standard errors associated with these point estimates.The methods thus allow for a unified inferential framework, where point estimates and statistical inference are based on the same methods. Using the new estimates, we investigate U.S. stock returns and find that some variables are able to predict stock returns.

Suggested Citation

  • Kurozumi, Eiji & Aono, Kohei, 2013. "Estimation And Inference In Predictive Regressions," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 54(2), pages 231-250, December.
    • Handle: RePEc:hit:hitjec:v:54:y:2013:i:2:p:231-250
    DOI: 10.15057/26018
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    More about this item

    Keywords

    near unit root; bias; stock return; jackknife;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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