A Naive Least Squares Method for Spatial Autoregression with Covariates

发布时间：2018-03-16 作者：浏览次数：

Speaker:	潘蕊	DateTime:	2018年3月17日（周六）上午9:30–10：30
Brief Introduction to Speaker: 潘蕊，中央财经大学副教授。		Place:	六号楼二楼报告厅
Abstract:Due to the rapid development of social networks, the spatial autoregression model with covariates is increasingly being applied in practice. However, traditional estimation methods such as maximum likelihood estimation are practically infeasible if the network size n is very large. Here, we propose a novel estimation approach, that reduces the computational complexity from O(n3) to O(n). This approach is developed by ignoring the endogeneity issue induced by network dependence. We show that the resulting estimator is consistent and asymptotically normal under certain conditions. Extensive simulation studies are presented to demonstrate its finite sample performance and a real social network dataset is analyzed for illustration purposes.