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International Regional Science Review
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Estimating Spatial Autoregressive Models by GME-GCE Techniques

Esteban Fernández-Vázquez

University of Oviedo, Oviedo, Spain

Matías Mayor-Fernández

University of Oviedo, Oviedo, Spain

Jorge Rodríguez-Vález

BBVA Banking Group, Bilbao, Spain

The traditional approach to estimate spatial models bases on a preconceived spatial weights matrix to measure spatial interaction among locations. The a priori assumptions used to define this matrix are supposed to be in line with the ``true'' spatial relationships among the locations of the dataset. Another possibility consists of using some information present on the sample data to specify an empirical matrix of spatial weights. In this article we propose to estimate spatial autoregressive models by generalized maximum entropy (GME) and generalized cross entropy (GCE) econometrics. We compare some traditional methodologies with the proposed GME-GCE estimator by means of Monte Carlo simulations in several scenarios. The results show that the entropy-based estimation techniques can outperform traditional approaches under some circumstances. An empirical case is also studied to illustrate the implementation of the proposed techniques for a real-world example.

Key Words: spatial econometrics • generalized maximum entropy—generalized cross entropy econometrics • spatial autoregressive models • Monte Carlo simulations

This version was published on April 1, 2009

International Regional Science Review, Vol. 32, No. 2, 148-172 (2009)
DOI: 10.1177/0160017608326600
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