A Simple Approach for Diagnosing Instabilities in Predictive Regressions
A Simple Approach for Diagnosing Instabilities in Predictive Regressions
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DOI：
10.1111/obes.12184
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发表时间：
2017-10
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通讯作者：
Jean-Yves Pitarakis
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作者：
Jean-Yves Pitarakis
我们介绍了一种方法，用于检测时间变化和预测回归参数中不稳定性的存在，这些参数将噪声变量（如股票收益）与高度持久的预测变量（如股票市场估值比率）联系起来。我们提出的方法依赖于基于最小二乘的预测回归的平方残差，实现起来很简单。更重要的是，我们的测试统计量的分布被证明没有讨厌的参数，已经在文献中列出，并且对于所选择的预测器的持久性程度是稳健的。我们提出的方法随后应用于美国股票月度回报的可预测性，包括股息收益率、股息支付、收益价格、股息价格和账面市值比。我们的研究结果有力地支持了1927年至2013年期间不稳定因素的存在，但也清楚地指出，这些不稳定因素在50年代中期之后消失了。关键字;股票收益的可预测性，结构性断裂，CUSUMSQ，预测性回归
We introduce a method for detecting the presence of time variation and instabilities in the parameters of predictive regressions linking noisy variables such as stock returns to highly persistent predictors such as stock market valuation ratios. Our proposed approach relies on the least squares based squared residuals of the predictive regression and is trivial to implement. More importantly the distribution of our test statistic is shown to be free of nuisance parameters, is already tabulated in the literature and is robust to the degree of persistence of the chosen predictor. Our proposed method is subsequently applied to the predictability of monthly US stock returns with the dividend yield, dividend payout, earnings-price, dividend-price and book-to-market value ratios. Our results strongly support the presence of instabilities over the 1927-2013 period but also clearly point to the disappearance of these after the mid 50s. Keywords; predictability of stock returns, structural breaks, CUSUMSQ, predictive regressions