NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS
提出一种在预测回归模型中处理异质持续波动、内生性、持久性和厚尾性的稳健推断方法，结合非线性工具变量估计和波动校正，适用于连续和离散时间模型，模拟显示有限样本性质优于常用方法。
We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively. Our method is simple to implement and is applicable both in the case of continuous and discrete time models. According to our simulation study, the proposed method performs well compared with widely used alternative inference procedures in terms of its finite sample properties in various dependence and persistence settings observed in real-world financial and economic markets.