A Simple Approach for Diagnosing Instabilities in Predictive Regressions
提出一种基于最小二乘残差平方和的简单方法，用于检测预测回归中参数的结构断点，适用于股票收益与估值比率等变量，且检验统计量分布无冗余参数、稳健于预测变量的持久性。
Abstract We introduce a method for detecting the presence of structural breaks in the parameters of predictive regressions linking noisy variables such as stock returns to persistent predictors such as valuation ratios. Our approach relies on the least squares‐based squared residuals of the predictive regression and is straightforward to implement. The distributions of the two test statistics we introduce are shown to be free of nuisance parameters, valid under dependent errors, already tabulated in the literature and robust to the degree of persistence of the chosen predictor. Our proposed method is subsequently applied to the predictability of US stock returns.