Trending Time-Varying Coefficient Spatial Panel Data Models

被引：0
|
作者：
Chang, Hsuan-Yu [1 ]
Song, Xiaojun [2 ,3 ]
Yu, Jihai [2 ]
机构：
[1] Peking Univ, Chung Hua Inst Econ Res, Ctr Green Econ, Beijing, Peoples R China
[2] Peking Univ, Guanghua Sch Management, Dept Business Stat & Econometr, Beijing, Peoples R China
[3] Peking Univ, Ctr Stat Sci, Beijing, Peoples R China
来源：
JOURNAL OF BUSINESS & ECONOMIC STATISTICS | 2024年
基金：
中国国家自然科学基金;
关键词：
Generalized method of moments; Nonlinear time trend; Spatial autoregressive models; Time-varying coefficients; SEMIPARAMETRIC ESTIMATION; AUTOREGRESSIVE MODEL; GMM ESTIMATION; SERIES;
D O I：
10.1080/07350015.2024.2340516
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
This article investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ various GMM estimations for regression coefficients with both linear and quadratic moments. Time trend estimator based on these GMM estimations is also proposed. Monte Carlo experiments show that the finite sample performance of the estimators is satisfactory. As an empirical illustration, we investigate the trending pattern of the spillover effect of air pollution among Chinese cities from 2015 to 2021.
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页数：13
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