Testing for a Common Factor in a Spatial Autoregression Model

Abstract

Three asymptotically equivalent tests for the presence of a common factor in a spatial autoregression model are presented. When such a common factor exists, the model reduces to the simple regression with spatially correlated disturbances. The tests can thus be used to examine the appropriateness of the latter specification. The procedure is illustrated by application to a model of Irish agricultural consumption discussed by O'Sullivan (1968), and Cliff and Ord (1973).

Matlab Software for Spatial Panels

“…This model can then be used to test the hypotheses H 0 : θ=0 and H 0 : θ+λβ=0. The first hypothesis examines whether the spatial Durbin can be simplified to the spatial lag model, and the second hypothesis whether it can be simplified to the spatial error model (Burridge, 1981 This is because this model generalizes both the spatial lag and the spatial error model.…”

A spatial econometric study on effects of fiscal and financial supports for agriculture in China

“…As shown in Table A3, it is more appropriate to adopt the Durbin model with the fixed effects estimation. In the fourth step, we have the Wald test of corresponding model in order to make sure if the spatial Durbin model can be simplified for the spatial lag model or the spatial error model (Burridge 1981). The results of the Wald test (Table A3) show that the model cannot be simplified for the spatial error model or the spatial lag model, so the final model is the spatial Durbin Model with the spatial and time-period specific effects： for i = 1, 2,…, N. Equation (12) is the spatial econometric model of fiscal and financial supports for agriculture in China from 1997 to 2010.…”

Model Comparison and Model Validation Issues in Empirical Work on Urban Density Functions

“…This procedure is derived from the so-called spatial common factor model (Anselin 1980, Burridge 1981, Bivand 1984, or, in other words, from the equivalence of a spatial autoregressive formulation,…”