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Non-Stationary Parametric Single-Index Predictive Models: Simulation and Empirical Studies

Ying Zhou (Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, Australia)

Hsein Kew (Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, Australia)

Jiti Gao (Department of Econometrics and Business Statistics, Monash University, Clayton, VIC, Australia)

Essays in Honor of Joon Y. Park: Econometric Theory

ISBN: 978-1-83753-209-4, eISBN: 978-1-83753-208-7

Publication date: 24 April 2023

Abstract

This chapter considers the estimation of a parametric single-index predictive regression model with integrated predictors. This model can handle a wide variety of non-linear relationships between the regressand and the single-index component containing either the cointegrated predictors or the non-cointegrated predictors. The authors introduce a new estimation procedure for the model and investigate its finite sample properties via Monte Carlo simulations. This model is then used to examine stock return predictability via various combinations of integrated lagged economic and financial variables.

Keywords

Acknowledgements

Acknowledgments

The authors thank Yoosoon Chang, two anonymous referees and Gael Martin for helpful suggestions on an earlier draft. This chapter is based on the second chapter of the first author’s doctoral dissertation at Monash University. The third author also acknowledges the financial support by the Australian Research Council Discovery Grants Program under Grant Number: DP170104421.

Citation

Zhou, Y., Kew, H. and Gao, J. (2023), "Non-Stationary Parametric Single-Index Predictive Models: Simulation and Empirical Studies", Chang, Y., Lee, S. and Miller, J.I. (Ed.) Essays in Honor of Joon Y. Park: Econometric Theory (Advances in Econometrics, Vol. 45A), Emerald Publishing Limited, Leeds, pp. 349-365. https://doi.org/10.1108/S0731-90532023000045A012

Publisher

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Emerald Publishing Limited