Robust inference with stochastic local unit root regressors in predictive regressions
Robust inference with stochastic local unit root regressors in predictive regressions
This paper explores predictive regression models with stochastic unit root (STUR) components and robust inference procedures that encompass a wide class of persistent and time-varying stochastically nonstationary regressors. The paper extends the mechanism of endogenously generated instrumentation known as IVX, showing that these methods remain valid for short and long-horizon predictive regressions in which the predictors have STUR and local STUR (LSTUR) generating mechanisms. Both mean regression and quantile regression methods are considered. The asymptotic distributions of the IVX estimators are new and require some new methods in their derivation. The distributions are compared to previous results and, as in earlier work, lead to pivotal limit distributions for Wald testing procedures that remain robust for both single and multiple regressors with various degrees of persistence and stochastic and fixed local departures from unit roots. Numerical experiments corroborate the asymptotic theory, and IVX testing shows good power and size control. The IVX methods are illustrated in an empirical application to evaluate the predictive capability of economic fundamentals in forecasting S&P 500 excess returns.
IVX, LSTUR, Long horizon, Predictability, Quantile regression, Robustness, STUR, Short horizon
Liu, Yanbo
a9c4cfbb-6bcb-413b-b8ff-0b848ae2809a
Phillips, Peter C.b.
f67573a4-fc30-484c-ad74-4bbc797d7243
4 July 2022
Liu, Yanbo
a9c4cfbb-6bcb-413b-b8ff-0b848ae2809a
Phillips, Peter C.b.
f67573a4-fc30-484c-ad74-4bbc797d7243
Liu, Yanbo and Phillips, Peter C.b.
(2022)
Robust inference with stochastic local unit root regressors in predictive regressions.
Journal of Econometrics.
(doi:10.1016/j.jeconom.2022.06.002).
Abstract
This paper explores predictive regression models with stochastic unit root (STUR) components and robust inference procedures that encompass a wide class of persistent and time-varying stochastically nonstationary regressors. The paper extends the mechanism of endogenously generated instrumentation known as IVX, showing that these methods remain valid for short and long-horizon predictive regressions in which the predictors have STUR and local STUR (LSTUR) generating mechanisms. Both mean regression and quantile regression methods are considered. The asymptotic distributions of the IVX estimators are new and require some new methods in their derivation. The distributions are compared to previous results and, as in earlier work, lead to pivotal limit distributions for Wald testing procedures that remain robust for both single and multiple regressors with various degrees of persistence and stochastic and fixed local departures from unit roots. Numerical experiments corroborate the asymptotic theory, and IVX testing shows good power and size control. The IVX methods are illustrated in an empirical application to evaluate the predictive capability of economic fundamentals in forecasting S&P 500 excess returns.
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More information
Accepted/In Press date: 6 June 2022
e-pub ahead of print date: 4 July 2022
Published date: 4 July 2022
Additional Information:
Funding Information:
Thanks are due to the CoEditor, Torben Andersen, and two referees for helpful comments on the paper and guidance for revision. We also thank Jia Li, Nan Liu, Tassos Magdalinos, Liangjun Su, Katsuto Tanaka, Qiying Wang, Jun Yu, Yichong Zhang and the participants of the 2019 SETA, the 2019 AMES, and the 2019 CMES conferences for suggestions and discussions. Phillips acknowledges support from a Lee Kong Chian Fellowship, Singapore at SMU, the Kelly Fund, United States of America at the University of Auckland, and the National Science Foundation, United States of America under Grant No. SES 18-50860 .
Publisher Copyright:
© 2022 Elsevier B.V.
Keywords:
IVX, LSTUR, Long horizon, Predictability, Quantile regression, Robustness, STUR, Short horizon
Identifiers
Local EPrints ID: 468798
URI: http://eprints.soton.ac.uk/id/eprint/468798
ISSN: 0304-4076
PURE UUID: 53288d8f-13b3-4a45-a0ca-fd6961cf9e1f
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Date deposited: 25 Aug 2022 17:22
Last modified: 16 Mar 2024 21:23
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Author:
Yanbo Liu
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