A0653
Title: Endogenous weights in Spatial autoregression
Authors: Jungyoon Lee - Royal Holloway, University of London (United Kingdom) [presenting]
Offer Lieberman - Bar-Ilan Univesity (Israel)
Francesca Rossi - University of Verona (Italy)
Gianfranco Piras - West Virginia University (United States)
Abstract: Spatial models are generally specified in terms of exogenous weighting matrices. When the exogeneity assumption is not satisfied, typical model specifications and conventional estimation procedures are no longer valid. Possible violations of the exogeneity assumption occur e.g. in the presence of omitted variables or when the spatial weighting matrix is measured with error. In this paper we establish asymptotic theory for a spatialautoregressive (SAR) model where the network structure is possibly endogenous and the spatial parameter can be less than or equal to unity. Consistency of the two stage least squares estimator is proven and its asymptotic distribution is derived, including in the case of the so-called spatial unit root. We also provide a test of exogeneity of the weight matrix.
