New Robust Inference for Predictive Regressions
50 Pages Posted: 3 Jun 2020 Last revised: 1 Nov 2021
Date Written: October 29, 2021
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: Suggested Citation