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A revisit to bias-adjusted predictive regression

Author

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  • Xu, Ke-Li

Abstract

We consider robust inference of predictive regression based on bias correction. We propose new variance estimators which can accommodate conditionally heteroskedastic and serially correlated errors, and predictors with unspecified dependence structure. We also present a previously overlooked robustness property of the existing variance estimator. Empirically we illustrate the methods with a classical application to stock return and dividend growth predictability.

Suggested Citation

  • Xu, Ke-Li, 2025. "A revisit to bias-adjusted predictive regression," Journal of Empirical Finance, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:empfin:v:80:y:2025:i:c:s0927539824001129
    DOI: 10.1016/j.jempfin.2024.101578
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    More about this item

    Keywords

    Bias correction; Conditional heteroskedasticity; Predictive regression; Robust inference;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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