论文标题
预测回归的新鲁棒推断
New robust inference for predictive regressions
论文作者
论文摘要
我们提出了两种可靠的方法，用于在异质和持续的波动性以及内源性，持久性和/或脂肪尾回归和误差下以及内源性，持久性和/或误差下测试预测回归模型未知参数的假设。在离散时间模型和连续时间模型的情况下，提出的强大测试方法既适用。这两种方法都使用Cauchy估计器有效处理回归和错误中内生性，持久性和/或脂肪尾部的问题。我们两种方法之间的区别是如何控制异质波动率。第一种方法使用Ibragimov和Muller中提出的回归参数的组估计量依赖于鲁棒的T统计推断，2010年。实施很容易，但需要外源性波动性假设。为了放松外源性波动性假设，我们提出了另一种依赖于挥发性非参数校正的方法。与广泛使用的替代推理程序相比，所提出的方法在其有限样本特性方面进行了很好的表现。
We propose two robust methods for testing hypotheses on unknown parameters of predictive regression models under heterogeneous and persistent volatility as well as endogenous, persistent and/or fat-tailed regressors and errors. The proposed robust testing approaches are applicable both in the case of discrete and continuous time models. Both of the methods use the Cauchy estimator to effectively handle the problems of endogeneity, persistence and/or fat-tailedness in regressors and errors. The difference between our two methods is how the heterogeneous volatility is controlled. The first method relies on robust t-statistic inference using group estimators of a regression parameter of interest proposed in Ibragimov and Muller, 2010. It is simple to implement, but requires the exogenous volatility assumption. To relax the exogenous volatility assumption, we propose another method which relies on the nonparametric correction of volatility. The proposed methods perform well compared with widely used alternative inference procedures in terms of their finite sample properties.
