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统计机器学习与因果效应评估系列学术报告(1) 香港大学于萍副教授报告

发布时间:2023-05-19文章来源: 浏览次数:

报告题目Robust Inference for Misspecified Threshold Regression and Regression Tree Analysis

报告人:于萍 教授 (香港大学)

报告时间2023年5月31日上午 10:00-11:00



In this paper, we develop the asymptotic theory for threshold regression under misspecification, which is especially useful in the regression tree analysis of machine learning. First, we provide a thorough characterization of the asymptotic distribution of the least square estimator, which integrates some fragmented asymptotic results of threshold regression in the literature into one unified framework of misspecification. The asymptotic distribution depends on the fitted threshold regression model being discontinuous or continuous and also on the rate of the limit objective function shrinking to zero in the direction of threshold parameter. Second, we provide a LR-based inference method for the threshold point, which can be treated as a misspecification-robust extension of the method in Hansen (2000, Econometrica, 68, 575-603). Two empirical applications illustrate the usefulness of our new inference method.

报告人简介:香港大学经济及工商管理学院副教授,博士生导师。2009年博士毕业于美国威斯康星大学麦迪逊分校2009-2014年新西兰奥克兰大学做助理教授。主持过香港港府基金的项目4项,已发表学术论文17篇,其中多篇发表于Journal of Econometrics, Econometric TheoryJournal of Business and Economic Statistics


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