Robust Two-Stage Estimation in General Spatial Dynamic Panel Data Models

作     者：DING Hao JIN Baisuo WU Yuehua DING Hao;JIN Baisuo;WU Yuehua

作者机构：Department of Mathematics and StatisticsYork UniversityTorontoOntarioM3J 1P3Canada Department of Statistics and FinanceSchool of ManagementUniversity of Science and Technology of ChinaHefei230026China 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2023年第36卷第6期

页      面：2580-2604页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by the Natural Sciences and Engineering Research Council of Canada under Grant No.RGPIN-2017-05720 the National Natural Science Foundation under Grant Nos.12201601,71873128,11571337,71631006,and 71921001 the Anhui Provincial Natural Science Foundation under Grant No.2208085QA06 

主　　题：Asymptotic normality consistency M-estimation model selection outliers 

摘      要：This paper proposes a robust two-stage estimation procedure for a general spatial dynamic panel data model in light of the two-stage estimation procedure in Jin,et al.(2020).The authors replace the least squares estimation in the first stage of Jin,et al.(2020)by *** authors also provide the justification for not making any change in its second stage when the number of time periods is large *** proposed methodology is robust and efficient,and it can be easily *** addition,the authors study the limiting behavior of the parameter estimators,which are shown to be consistent and asymptotic normally distributed under some *** simulation studies are carried out to assess the proposed procedure and a COVID-19 data example is conducted for illustration.