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Robust Inference for Multiple Predictive Regressions with an Application on Bond Risk Premia

2024/01/02 by Xiaosai Liao, Liao, Xiaosai, Xinjue Li +3
Economics, Econometrics and Finance · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Market Dynamics and Volatility #Methodology (stat.ME) #Monetary Policy and Economic Impact

paper · pdf · doi:10.48550/arxiv.2401.01064

openalex publication_date 2024/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a robust hypothesis testing procedure for the predictability of multiple predictors that could be highly persistent. Our method improves the popular extended instrumental variable (IVX) testing (Phillips and Lee, 2013; Kostakis et al., 2015) in that, besides addressing the two bias effects found in Hosseinkouchack and Demetrescu (2021), we find and deal with the variance-enlargement effect. We show that two types of higher-order terms induce these distortion effects in the test statistic, leading to significant over-rejection for one-sided tests and tests in multiple predictive regressions. Our improved IVX-based test includes three steps to tackle all the issues above regarding finite sample bias and variance terms. Thus, the test statistics perform well in size control, while its power performance is comparable with the original IVX. Monte Carlo simulations and an empirical study on the predictability of bond risk premia are provided to demonstrate the effectiveness of the newly proposed approach.

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