A Unified test for the Intercept of a Predictive Regression Model

Xiaohui Liu, Yuzi Liu, Yao Rao and Fucai Lu

Oxford Bulletin of Economics and Statistics, 2021, vol. 83, issue 2, 571-588

Abstract: Testing the predictability of the predictive regression model is of great interest in economics and finance. Recently, (Zhu et al. (2014) Predictive regressions for macroeconomic data, Vol. 8, pp. 577–594.) proposed a unified test to account for this issue. Their test has a desirable property that its limit distribution is standard regardless of the regressor being stationary, near unit root or unit root. However, this test depends on, a priori, whether there is an intercept in the predictive regression while this is usually unknown in practice. In this paper, using empirical likelihood inference, we develop a unified pretest for the intercept, as a pretest to determine the choice of the predictability test. Simulations studies confirm that the proposed pretest works well. Two real data examples are also provided to illustrate the importance of such pretest. The first revisits the S&P 500 index data and the second investigates stock return predictability and investor sentiment for six countries.

Date: 2021
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