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.
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