【主题】A Machine Learning Approach to Weighted Least Squares Estimation
【报告人】李红军 (副教授, 首都经济贸易大学)
【时间】5月28日（星期二） 15：30-17：00
【地点】经济学院701
【语言】英文
【摘要/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 approaches and poor finite-sample performance for nonparametric methods. 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.
