【摘要】LeSage and Pace (2009) consider the impact of omitted variables in the face ofspatial dependence in the disturbance process of a linear regression relationship and show thatthis can lead to a spatial Durbin model. Monte Carlo experiments and Bayesian model com-parison methods are used to distinguish between spatial error and Durbin model speci?cationsthat arise with varying levels of correlation between included and omitted variables. The MonteCarlo results suggest use of the common factor relationship developed in Burridge (1981) as away to test for the presence of omitted variables bias in?uencing speci?c explanatory variables.

【关键词】Common factor relationship;global spatial spillovers;Bayesian model comparisonmethods

【文献来源】Donald J. Lacombe;James P. LeSage.Papers in Regional Studies.2015（2）