Gaussian Inference in Predictive Regressions for Stock Returns
针对预测回归中因预测变量持续性和内生性导致的非正态分布问题，通过构建两种M估计t统计量的线性组合，得到标准正态检验统计量，并设计固定回归量自助法避免多重检验问题。对美国股票收益数据的实证发现，在二战、石油危机和大衰退等波动周期中存在可预测性。
Abstract Predictive regressions are an important tool in empirical finance. Under persistent predictors and so-called predictive regression endogeneity, OLS-based estimators and tests exhibit nonnormal limiting distributions. M estimators in such predictive regressions inherit these traits. The limiting distributions of different M estimators and M estimation-based tests of predictability depend on the same non-standard component. We exploit this to eliminate the nonstandard component and obtain standard normal test statistics of no predictability by building suitable linear combinations of two different M-based t ratios. This further enables us to set up a fixed-regressors bootstrap procedure to avoid the multiple-testing problem when applying the new test in rolling subsamples. Examining the predictability of U.S. stock returns, we find evidence for stock return predictability in volatile business cycle periods, such as World War II, Oil Crisis and Great Recession.