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A system of time-varying models for predictive regressions

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  • Yu, Deshui
  • Yan, Yayi

Abstract

This paper proposes a system of time-varying models for predictive regressions, where a time-varying autoregressive (TV-AR) process is introduced to model the dynamics of the predictors and a linear control function approach is used to improve the estimation efficiency. We employ a profile likelihood estimation method to estimate both constant and time-varying coefficients and propose a hypothesis test to examine the parameter stability. We establish the asymptotic properties of the proposed estimators and test statistics accordingly. Monte Carlo simulations show that the proposed methods work well in finite samples. Empirically, the TV-AR process effectively approximates the time-series behavior of a broad set of potential predictors. Furthermore, we reject the stability assumption of predictive models for more than half of these predictors. Finally, the linear projection method not only improves estimator efficiency but also enhances out-of-sample forecasting performance, leading to significant utility gains in forecasting experiments.

Suggested Citation

  • Yu, Deshui & Yan, Yayi, 2025. "A system of time-varying models for predictive regressions," Journal of Empirical Finance, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:empfin:v:82:y:2025:i:c:s0927539825000441
    DOI: 10.1016/j.jempfin.2025.101622
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    References listed on IDEAS

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: 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
    • G1 - Financial Economics - - General Financial Markets

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