报告题目：Statistical inference for autoregressive models under heteroscedasticity of unknown form

报 告 人：朱柯助理教授 香港大学

报告时间：2020年6月8日 16:00-17:00

报告地点：腾讯会议 ID：876 941 401

密码: 0608

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https://meeting.tencent.com/s/rEwi0BPc3gwy

校内联系人：朱复康 fzhu@jlu.edu.cn

报告摘要：

This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the asymptotic normality of the weighted least absolute deviations estimator (LADE) for the model. Second, we develop the random weighting (RW) method to estimate its asymptotic covariance matrix, leading to the implementation of the Wald test. Third, we construct a portmanteau test for model checking, and use the RW method to obtain its critical values. As a special weighted LADE, the feasible adaptive LADE (ALADE) is proposed and proved to have the same efficiency as its infeasible counterpart. The importance of our entire methodology based on the feasible ALADE is illustrated by simulation results and the real data analysis on three U.S. economic data sets.

报告人简介：

朱柯，香港大学统计与精算系的助理教授、博士生导师，于2011年获得香港科技大学统计学博士学位。主要研究方向为时间序列、计量经济和统计，包括稳健统计、拟合优度检验、变点问题、bootstrap方法及应用计量经济。目前，他已经发表学术论文20余篇，其中包括Annals of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B, Journal of Econometrics, Econometric Theory, Journal of Business and Economic Statistics, Statistica Sinica等国际顶尖统计和计量经济学期刊。