Robust block bootstrap panel predictability tests
提出了两种基于块自助法的面板可预测性检验方法，允许截面相关、异质预测斜率、持久预测变量和复杂误差结构，适用于检验股票收益等金融数据的可预测性。
This article develops two block bootstrap-based panel predictability test procedures that are valid under very general conditions. Some of the allowable features include cross-sectional dependence, heterogeneous predictive slopes, persistent predictors, and complex error dynamics, including cross-unit endogeneity. While the first test procedure tests if there is any predictability at all, the second procedure determines the units for which predictability holds in case of a rejection by the first. A weak unit root framework is adopted to allow persistent predictors, and a novel theory is developed to establish asymptotic validity of the proposed bootstrap. Simulations are used to evaluate the performance of our tests in small samples, and their implementation is illustrated through an empirical application to stock returns.