Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model  

作　　者：LUO Guowang WU Mixia PANG Zhen 

机构地区：[1]College of Big Data Statistics,Guizhou University of Finance and Economics,Guiyang 550025,China [2]College of Statistics and Data Science,Faculty of Science,Beijing University of Technology,Beijing 100124,China [3]Department of Applied Mathematics,Hong Kong Polytechnic University,Hong Kong,China

出　　处：《Journal of Systems Science & Complexity》2021年第6期2310-2333,共24页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant No.11771032;Scientific Research Foundation for Young Talents of Department of Education of Guizhou Province;Scientific Research Foundation of Guizhou University of Finance and Economics under Grant No.2021YJ027。

摘　　要：In this paper empirical likelihood(EL)-based inference for a semiparametric varyingcoefficient spatial autoregressive model is investigated.The maximum EL estimators for the parametric component and the nonparametric component are established.Furthermore,asymptotic properties of the proposed estimators and EL ratios are derived,and the corresponding confidence regions/bands are constructed.Their finite sample performances are studied via simulation and an example.

关 键 词：Confidence regions empirical likelihood instrumental variable residual-adjusted Wilks theorem 

分 类 号：O212.1[理学—概率论与数理统计]