Trending Time-Varying Coefficient Spatial Panel Data Models
研究了回归系数向量随时间趋势变化的空间面板数据模型，提出基于GMM的估计方法，并用中国城市空气污染溢出效应的趋势模式进行实证。
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.