经济与社会研究院SEMINAR第158期

题目：A New Approach to Test Predictability in Quantile Regressions with Persistent Predictors

主讲人：陈海强，厦门大学

时间：2019年5月22日，13:30-15:00

地点：暨南大学中惠楼106B室

主讲人简介：

陈海强，经济学博士、教授、博士生导师，目前任职于厦门大学王亚南经济研究院以及厦门大学经济学院金融系，现担任计量经济学教育部重点实验室副主任，厦门大学经济学科数据科学与决策咨询中心主任，国家自然科学基金《防范和化解金融风险》应急管理项目负责人，福建省高等学校新世纪优秀人才，厦门市高层次引进人才。2003年毕业于北京大学，获经济学与统计学双学士学位；2005年毕业于香港中文大学，获经济学硕士；2011年毕业于美国康奈尔大学，获经济学博士。研究领域为金融科技、金融计量、大数据分析、量化金融等，先后在《经济研究》《金融研究》《管理科学学报》、 Econometric Theory, Journal of Empirical Finance， Journal of International Money and Finance等国内外顶尖期刊发表论文几十篇。陈海强教授作为负责人和主要参与者承担了十余项国家级、省部级和企业科研课题研究。

Abstract:

For predictive quantile regressions with highly persistent regressors, the traditional test statistics based on least squared estimators lose their validity and their limiting distribution relies on the unknown persistence parameters of predictors. This paper proposes a novel econometric method to offer a robust inference theory across all types of persistent regressors. Particularly, we construct a weighted estimator based on a quantile regression with an auxiliary regressor, which is generated as a combination of an exogenous simulated nonstationary process and a bounded transformation of the original regressor. Under some mild conditions, we show that the self-normalized test statistics based on the weighted estimator converge to a standard normal or Chisq distribution. Comparing to the existing approach, our method could reach the local power under the optimal rate T with non-stationary predictors and√T with stationary predictors respectively. Moreover, the method can be easily generalized to multiple regressors with mixed persistence degree. Simulations and empirical studies are provided to demonstrate the effectiveness of the newly proposed approach. The heterogeneous predictability of US stock returns at different quantile levels is reexamined.