Robust inference with stochastic local unit root regressors in predictive regressions

被引:0
作者
Liu, Yanbo [1 ]
Phillips, Peter C. B. [2 ,3 ,4 ,5 ,6 ]
机构
[1] Shandong Univ, Sch Econ, 27 Shanda Nanlu, Jinan 250100, Shandong, Peoples R China
[2] Yale Univ, New Haven, CT USA
[3] Univ Auckland, Auckland, New Zealand
[4] Univ Southampton, Southampton, England
[5] Singapore Management Univ, Singapore, Singapore
[6] Yale Univ, Cowles Fdn Res Econ, Box 208281, New Haven, CT 06520 USA
基金
美国国家科学基金会;
关键词
IVX; Long horizon; LSTUR; Predictability; Quantile regression; Robustness; Short horizon; STUR; ECONOMETRIC INFERENCE; TESTS; PARAMETER; RETURNS;
D O I
10.1016/j.jeconom.2022.06.002
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper explores predictive regression models with stochastic unit root (STUR) components and robust inference procedures that encompass a wide class of persis-tent 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.& COPY; 2022 Elsevier B.V. All rights reserved.
引用
收藏
页码:563 / 591
页数:29
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