包含随机单位根参数的预测回归模型的稳健推断（Robust Inference with Stochastic Local Unit Root Regressors in Predictive Regressions）

主讲人：

刘彦伯（山东大学）

主持老师：

王熙（北大经院）

参与老师：

（北大经院）王一鸣、刘蕴霆

（北大国发院）沈艳、黄卓、张俊妮、孙振庭

（北大新结构经济学研究院）胡博

时间：

2021年9月24日（周五） 10:00 AM -- 11:30 AM

地点（线下）：经院107会议室

主讲人简介：

刘彦伯，山东大学经济学院助理教授。他于2015年毕业于北京师范大学，获硕士学位； 2020年毕业于新加坡管理大学，获博士学位。近年来从事计量经济学理论，金融计量经济学，机器学习的研究。

摘要：

This paper explores predictive regression models with stochastic unit root (STUR) components and robust inference procedures that encompass a wide class of persistent and time-varying stochastically nonstationary regressors. The paper extends the mechanism of endogenously generated instrumentation known as IVX, showing that these methods remain valid for short and long-horizon predictive regressions in which the predictors have STUR and local STUR (LSTUR) generating mechanisms. Both mean regression and quantile regression methods are considered. The asymptotic distributions of the IVX estimators are new and require some new methods in their derivation. The distributions are compared to previous results and, as in earlier work, lead to pivotal limit distributions for Wald testing procedures that remain robust for both single and multiple regressors with various degrees of persistence and stochastic and fixed local departures from unit roots. Numerical experiments corroborate the asymptotic theory, and IVX testing shows good power and size control. The IVX methods are illustrated in an empirical application to evaluate the predictive capability of economic fundamentals in forecasting S&P 500 excess returns.