A System of Time-Varying Models for Predictive Regressions

45 Pages Posted: 15 Apr 2021

See all articles by Deshui Yu

Deshui Yu

College of Finance and Statistics, Hunan University

Yayi Yan

Department of Econometrics and Business Statistics, Monash University

Date Written: April 2, 2021

Abstract

This paper proposes a system of semiparametric time-varying models for predictive regressions, where a locally stationary process in the form of time-varying autoregression is introduced to model varying-persistent predictors, and parameter instability and embedded endogeneity have also been taken into account simultaneously. We employ a semiparametric profile likelihood approach to
estimate both constant parameters and time-varying functional coefficients, and we further establish the asymptotic theory of the estimators in the system. Monte Carlo simulations show that the proposed estimation method works very well in finite samples. Empirically, we find that the popular predictors considered in the literature are well approximated by a time-varying first-order autoregressive process, those predictors generally contain significant and time-varying predictive content of future equity premium, and taking embedded endogeneity into account helps to identify the existence of return predictability.

Keywords: Return predictability, time-varying persistence, local stationarity, embedded endogeneity

JEL Classification: C14, C22, C58, G1

Suggested Citation

Yu, Deshui and Yan, Yayi, A System of Time-Varying Models for Predictive Regressions (April 2, 2021). Available at SSRN: https://ssrn.com/abstract=3818009 or http://dx.doi.org/10.2139/ssrn.3818009

Deshui Yu (Contact Author)

College of Finance and Statistics, Hunan University ( email )

2 Lushan South Rd
Changsha, CA Hunan 410082
China

Yayi Yan

Department of Econometrics and Business Statistics, Monash University ( email )

900 Dandenong Road
Caulfield, Victoria 3145
Australia
0498101110 (Phone)

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