Nonlinear Predictability of Stock Returns? Parametric Versus Nonparametric Inference in Predictive Regressions
研究了预测回归中非参数检验方法在低和高预测变量持续性下的表现，提出基于工具变量推断的两步预测法，在标普500数据中优于其他方法。
Nonparametric test procedures in predictive regressions have χ2 limiting null distributions under both low and high regressor persistence, but low local power compared to misspecified linear predictive regressions. We argue that IV inference is better suited (in terms of local power) for analyzing additive predictive models with uncertain predictor persistence. Then, a two-step procedure is proposed for out-of-sample predictions. For the current estimation window, one first tests for predictability; in case of a rejection, one predicts using a nonlinear regression model, otherwise the historic average of the stock returns is used. This two-step approach performs better than competitors (though not by a large margin) in a pseudo-out-of-sample prediction exercise for the S&P 500.