“Theory and Practice of Spatial Econometrics”

This text provides an introduction to spatial econometric theory along withnumerous applied illustrations of the models and methods described. The ap-plications utilize a set of MATLAB functions that implement a host of spatialeconometric estimation methods. The intended audience is faculty,students andpractitioners involved in modeling spatial data sets. The MATLAB functionsdescribed in this book have been used in my own research as well as teach-ing both undergraduate and graduate econometrics courses. They are availableon the Internet at http://www.econ.utoledo.edu along with the data sets andexamples from the text.The theory and applied illustrations of conventional spatial econometricmodels represent about half of the content in this text,with the other halfdevoted to Bayesian alternatives. Conventional maximum likelihood estimationfor a class of spatial econometric models is discussed in one chapter,followed bya chapter that introduces a Bayesian approach for this same set of models. Itis well-known that Bayesian methods implemented with a diffuse prior simplyreproduce maximum likelihood results,and we illustrate this point. However,the main motivation for introducing Bayesian methods is to extend the conven-tional models. Comparative illustrations demonstrate how Bayesian methodscan solve problems that confront the conventional models. Recent advances inBayesian estimation that rely on Markov Chain Monte Carlo (MCMC) methodsmake it easy to estimate these models. This approach to estimation has beenimplemented in the spatial econometric function library described in the text,so estimation using the Bayesian models require a single additional line in yourcomputer program.Some of the Bayesian methods have been introduced in the regional scienceliterature,or presented at conferences. Space and time constraints prohibit anydiscussion of implementation details in these forums. This text describes the im-plementation details,which I believe greatly enhance understanding and allowusers to make intelligent use of these methods in applied settings. Audienceshave been amazed (and perhaps skeptical) when I tell them it takes only 10seconds to generate a sample of 1,000 MCMC draws from a sequence of condi-tional distributions needed to estimate the Bayesian models. Implementationapproaches that achieve this type of speed are described here in the hope thatother researchers can apply these ideas in their own work.I have often been asked about Monte Carlo evidence for Bayesian spatiali