A new portmanteau test for predictive regression models with possible embedded endogeneity

• Published in: Journal of time series analysis 2024-11, Vol.45 (6), p.953-979
• Main Authors: Rao, Yao, Fan, Yawen, Ao, Huimin, Liu, Xiaohui
• Format: Article
• Language: English
• Subjects: Asymptotic methods; Correlation; Embedded endogeneity; Estimation bias; Innovations; jackknife empirical likelihood; Monte Carlo simulation; Performance prediction; predictive regression model; Regression models; serial correlation; Statistical analysis; Statistical inference; Tests
• ISSN: 0143-9782; 1467-9892
• DOI: 10.1111/jtsa.12745

In the widely used predictive regression model, any possible serial correlation in innovations leads to estimation bias and statistical inference distortions. Hence, it is important to pretest the existence of such serial correlation. Nevertheless, in the presence of embedded endogeneity, which is a common problem in the predictive regression setting, traditional serial correlation tests such as Box–Pierce (BP) and Ljung–Box (LB) tests are found to perform poorly. Motivated by this, we develop a new portmanteau test in this article as a pretest for serial correlation in predictive regression under possible embedded endogeneity. This test is based on the sample splitting idea and the jackknife empirical likelihood method. The asymptotic distribution of the proposed test has been derived, and the Monte Carlo simulations confirm good finite sample performances. As an illustration, we apply our proposed test in pretesting the serial correlation in predictive regression, where financial variables are used to predict the excess return of S&P 500.