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sidenav header background[9月24日] 计量金融与大数据分析workshop
发布日期：2021-09-23 04:56 来源：
时间：2021年9月24日（周五）10:00 AM -- 11:30 AM
主讲人：刘彦伯（山东大学）
题目：Robust Inference with Stochastic Local Unit Root Regressors in Predictive Regressions（包含随机单位根参数的预测回归模型的稳健推断）
地点：经院107
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆
报告摘要：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.
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。
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首页
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教学项目展开 / 收起
sidenav background教学项目
sidenav header background[9月24日] 计量金融与大数据分析workshop
发布日期：2021-09-23 04:56 来源：
时间：2021年9月24日（周五）10:00 AM -- 11:30 AM
主讲人：刘彦伯（山东大学）
题目：Robust Inference with Stochastic Local Unit Root Regressors in Predictive Regressions（包含随机单位根参数的预测回归模型的稳健推断）
地点：经院107
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆
报告摘要：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.
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。
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