Estimating Nonlinearities in Spatial Auregressive Models
Mardi | 2009-12-01 Nicolas DEBARSY – Vincenzo VERARDI – Amine LAHIANI In spatial autoregressive models, the functional form of autocorrelationis assumed to be linear. In this paper, we propose a simple semiparametricprocedure, based on Yatchew’s (1997) partial linear least squares, that doesnot impose this restriction. Simple simulations show that this model outper-forms traditional SAR estimation when nonlinearities are present. We thenapply the methodology on real data to test for the spatial pattern of vot-ing for independent candidates in US presidential […]