Robust Inference with Stochastic Local Unit Root Regressors in Predictive Regressions

Yanbo Liu and Peter Phillips
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Yanbo Liu: School of Economics, Shandong University

No 2305, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

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.

Keywords: IVX; Long horizon; LSTUR; Predictability; Quantile regression; Robustness; Short horizon; STUR (search for similar items in EconPapers)
JEL-codes: C12 C22 G01 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2021-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Note: Includes Supplimental Material
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Journal Article: Robust inference with stochastic local unit root regressors in predictive regressions (2023)
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