Abstract

Quantile regression is an effective tool in modeling data with heterogeneous conditional distribution. This paper considers the time-varying coefficient quantile predictive regressions with factor-augmented predictors, to capture smooth structural changes and incorporate high-dimensional data information in prediction simultaneously. Uniform consistency of the local linear quantile coefficient estimators is established under misspecification. To further improve the forecast accuracy, a novel time-varying model averaging based on local leave-h-out forward-validation is developed. The averaging estimator is shown to be asymptotically optimal in the sense of minimizing out-of-sample forecast risk function. Numerical results from simulations and a real data application to forecasting U.S. inflation demonstrate the nice performance of the averaging estimators.

Authors: Yundong Tu and Siwei Wang

个人简介

涂云东，北京大学光华管理学院和北京大学统计科学中心联席教授。入选“日出东方”北大光华青年人才，北京大学优秀博士学位论文指导教师，教育部“长江学者奖励计划”青年长江学者。2004年和2006年先后获武汉大学理学学士学位和经济学硕士学位，2012年获美国加州大学河滨分校经济学博士学位。亚太青年计量经济学者会议发起人和主要组织者。40余篇学术论文发表在多个国际国内知名专业杂志。著作教材《时间序列分析》由人民邮电出版社于2022年9月出版。主持多个国家自然科学基金项目，并担任自然科学基金匿名评审。曾获世界计量经济学会、加州计量经济学会议等学术组织提供的青年学者研究资助。研究领域涵盖时间序列分析、非参数计量方法、大数据分析、金融计量和预测等。