Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based
研究发现流行的缩放检验在预测变量不够持久时功效为零，提出基于短期模型隐含的新检验，模拟显示其有限样本表现优于多种常用检验，并重新评估了多个流行预测变量对股票溢价的预测能力。
Abstract Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.