New Robust Inference for Predictive Regressions

50 Pages Posted: 3 Jun 2020 Last revised: 1 Nov 2021

See all articles by Rustam Ibragimov

Rustam Ibragimov

affiliation not provided to SSRN

Jihyun Kim

Indiana University, Robert H. McKinney School of Law, Students

Anton Skrobotov

Russian Academy of National Economy and Public Administration under the President of the Russian Federation (RANEPA) - Department of Economics

Date Written: October 29, 2021

Abstract

We propose two robust methods for testing hypotheses on unknown parameters of predictive regression models under heterogeneous and persistent volatility as well as endogenous, persistent and/or fat-tailed regressors and errors. The proposed robust testing approaches are applicable both in the case of discrete and continuous time models. Both of the methods use the Cauchy estimator to effectively handle the problems of endogeneity, persistence and/or fat-tailedness in regressors and errors. The difference between our two methods is how the heterogeneous volatility is controlled. The first method relies on robust t-statistic inference using group estimators of a regression parameter of interest proposed in Ibragimov and Muller (2010). It is simple to implement, but requires the exogenous volatility assumption. To relax the exogenous volatility assumption, we propose another method which relies on the nonparametric correction of volatility. The proposed methods perform well compared with widely used alternative inference procedures in terms of their finite sample properties.

Keywords: predictive regression, robust inference, near nonstationarity, heavy tails, nonstationary volatility, endogeneity

JEL Classification: C12, C22

Suggested Citation

Ibragimov, Rustam and Kim, Jihyun and Skrobotov, Anton, New Robust Inference for Predictive Regressions (October 29, 2021). Available at SSRN: https://ssrn.com/abstract=3580937 or http://dx.doi.org/10.2139/ssrn.3580937

Rustam Ibragimov

affiliation not provided to SSRN

Jihyun Kim

Indiana University, Robert H. McKinney School of Law, Students ( email )

Indianapolis, ID 46202
United States

Anton Skrobotov (Contact Author)

Russian Academy of National Economy and Public Administration under the President of the Russian Federation (RANEPA) - Department of Economics ( email )

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