经济与社会研究院SEMINAR第150期

题目：A Machine Learning Approach to Weighted Least Squares Estimation

主讲人：李红军, 首都经济贸易大学

时间：2019年5月6日，15:00-16:15

地点：暨南大学中惠楼106B室

主讲人简介：

李红军，首都经济贸易大学国际经济管理学院副教授，博士就读于德州农工大学（Texas A&M University），师从计量经济学家李奇教授。现主要从事非参数计量、高维数据及机器学习等领域的研究，已有多篇论文发表在Journal of Econometrics, Economics Letters, Econometric Reviews和 Empirical Economics等经济学期刊上。

Abstract:

The traditional feasible weighted least square (WLS) estimation methods for linear regression models with conditional heteroscedasticity are unsatisfactory in certain sense, i.e., misspecification for parametric approach and poor finite-sample performance for nonparametric method. In this paper, we propose a machine learning approach to WLS estimation that can have better finite-sample performance than classical nonparametric approach while avoiding misspecification problem for the conditional variance. Similar to the nonparametric estimation, the new method also takes two steps. In the first step, we estimate the conditional variance using machine learning tools. In the second step, we estimate the linear coefficients using feasible WLS by plug in the unknown variance with its estimate. We show that the new estimator is consistent and asymptotically normally distributed. Also, we demonstrate the finite sample performce of the proposed method using kernel ridge regression.