A New Test for Multiple Predictive Regression
针对多个预测变量的回归模型，提出基于工具变量的新检验方法，解决现有检验随预测变量增多而发现虚假预测性的问题，并通过实证蒙特卡洛和股权溢价预测应用验证其有效性。
Abstract We consider inference for predictive regressions with multiple predictors. Extant tests for predictability (especially for joint predictability) may perform unsatisfactorily and tend to discover spurious predictability as the number of predictors increases. We propose a battery of new instrumental variables-based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation. A test based on the few-predictors-at-a-time parsimonious system approach is recommended. Empirical Monte Carlos demonstrates the remarkable finite-sample performance regardless of numerosity of predictors and their persistence properties. Empirical application to equity premium predictability is provided.