Impact Analysis for Spatial Autoregressive Models: With Application to Air Pollution in China

Abstract

: In this paper, we investigate impact analysis and its asymptotic inference for spatial autoregressive models. LeSage and Pace (2009) introduce impact analysis for spatial models and use Monte Carlo simulations to compute the dispersion. We propose to use the delta method, which enables us to obtain the dispersion in an explicit form. In addition, we provide the element-wise impact analysis. We first study the cross-sectional case, where various impacts are introduced to measure the interaction and feedback effects in a space dimension. We then study the spatial dynamic panel case with simultaneous spatial and dynamic feedback involved in the impacts. Monte Carlo results show that the proposed impact analysis has satisfactory finite sample properties. Finally, we apply impact analysis to investigate how meteorological factors and air pollutants affect PM 2 . 5 in Chinese cities.