A Unified test for the Intercept of a Predictive Regression Model*
针对预测回归模型中截距未知的问题，利用经验似然推断开发了一个统一预检验，帮助选择适当的可预测性检验方法，模拟和实例验证了其有效性。
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.