Inferring the Predictability Induced by a Persistent Regressor in a Predictive Threshold Model
针对持久回归变量可能引发的间歇性可预测性，开发了阈值效应预测回归模型的检验方法，无需事先知道其他参数是否存在阈值效应，并应用于股票回报可预测性分析。
We develop tests for detecting possibly episodic predictability induced by a persistent predictor. Our framework is that of a predictive regression model with threshold effects and our goal is to develop operational and easily implementable inferences when one does not wish to impose à priori restrictions on the parameters of the model other than the slopes corresponding to the persistent predictor. Differently put our tests for the null hypothesis of no predictability against threshold predictability remain valid without the need to know whether the remaining parameters of the model are characterized by threshold effects or not (e.g., shifting versus nonshifting intercepts). One interesting feature of our setting is that our test statistics remain unaffected by whether some nuisance parameters are identified or not. We subsequently apply our methodology to the predictability of aggregate stock returns with valuation ratios and document a robust countercyclicality in the ability of some valuation ratios to predict returns in addition to highlighting a strong sensitivity of predictability based results to the time period under consideration.