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Predictive regression with p-lags and order-q autoregressive predictors

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  • Jayetileke, Harshanie L.
  • Wang, You-Gan
  • Zhu, Min

Abstract

This paper considers predictive regressions, where yt is predicted by all p lags of xt, here with xt being autoregressive of order q, PR(p,q). The literature considers model properties in the cases where p=q. We demonstrate that the current augmented regression method can still reduce the bias in predictive coefficients, but its efficiency depends on correctly specifying both p and q. We propose an estimation framework for the predictive regression, PR(p,q), with a data-driven auto-selection of p and q to achieve the best bias reduction in predictive coefficients. The corresponding hypothesis testing procedure is also derived. The efficiency of the proposed method is demonstrated with simulations. Empirical applications to equity premium prediction illustrate the substantial difference between the estimates of our method and those obtained by the common predictive regressions with p=q.

Suggested Citation

  • Jayetileke, Harshanie L. & Wang, You-Gan & Zhu, Min, 2021. "Predictive regression with p-lags and order-q autoregressive predictors," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 282-293.
    • Handle: RePEc:eee:empfin:v:62:y:2021:i:c:p:282-293
    DOI: 10.1016/j.jempfin.2021.04.006
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    References listed on IDEAS

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    Cited by:

    1. Christis Katsouris, 2023. "Bootstrapping Nonstationary Autoregressive Processes with Predictive Regression Models," Papers 2307.14463, arXiv.org.

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    More about this item

    Keywords

    Predictive regressions; Bias; Augmented regression; Return predictability;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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