報告題目：Statistical inference for autoregressive models under heteroscedasticity of unknown form
報 告 人：朱柯助理教授 香港大學
報告時間：2020年6月8日 16:00-17:00
報告地點：騰訊會議 ID：876 941 401
密碼: 0608
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校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：
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.
報告人簡介：
朱柯，香港大學統計與精算系的助理教授、博士生導師，于2011年獲得香港科技大學統計學博士學位。主要研究方向為時間序列、計量經濟和統計，包括穩健統計、拟合優度檢驗、變點問題、bootstrap方法及應用計量經濟。目前，他已經發表學術論文20餘篇，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等國際頂尖統計和計量經濟學期刊。
