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This text provides an introduction to spatial econometric theory along with numerous applied illustrations of the models and methods described. The applications utilize a set of MATLAB functions that implement a host of spatial econometric estimation methods. The intended audience is faculty, students and practitioners involved in modeling spatial data sets. The MATLAB functions described in this book have been used in my own research as well as teaching both undergraduate and graduate econometrics courses. They are available on the Internet at http://www. econ. utoledo. edu along with the data sets and examples from the text.

The theory and applied illustrations of conventional spatial econometric models represent about half of the content in this text, with the other half devoted to Bayesian alternatives. Conventional maximum likelihood estimation for a class of spatial econometric models is discussed in one chapter, followed by a chapter that introduces a Bayesian approach for this same set of models. It is well-known that Bayesian methods implemented with a diffuse prior simply reproduce maximum likelihood results, and we illustrate this point. However, the main motivation for introducing Bayesian methods is to extend the conventional models. Comparative illustrations demonstrate how Bayesian methods can solve problems that confront the conventional models. Recent advances in Bayesian estimation that rely on Markov Chain Monte Carlo (MCMC) methods make it easy to estimate these models. This approach to estimation has been implemented in the spatial econometric function library described in the text, so estimation using …