Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. It explores a wide range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different circumstances. Leaders in this field, the authors clarify the often-mystifying phenomenon of simultaneous spatial dependence. By presenting new methods, they help with the interpretation of spatial regression models, especially ones that include spatial lags of the dependent variable. The authors also examine the relationship between spatiotemporal processes and long-run equilibrium states that are characterized by simultaneous spatial dependence. MATLAB(R) toolboxes useful for spatial econometric estimation are available on the authors' websites. This work covers spatial econometric modeling as well as numerous applied illustrations of the methods. It encompasses many recent advances in spatial econometric models-including some previously unpublished results.

詹姆斯 ∙ 勒沙杰（James LeSage），德克萨斯州立大学（圣马科斯）麦考伊工商管理学院金融经济系教授，经济学博士。勒沙杰教授是区域科学领域最有影响力的学者之一，也是该领域著作被引用次数最多的学者之一，在Journal of Econometrics, Journal of Applied Econometrics, Journal of Regional Science等国际知名学术期刊上发表论文60余篇，并为Journal of Political Economy, Review of Economics and Statistics等众多知名学术期刊审稿人。

凯利 ∙ 佩斯（R.Kelley Pace）是路易斯安那州立大学金融系教授，房地产学博士。佩斯教授的主要研究领域是房地产金融，已在Journal of Real Estate Finance and Economics, Real Estate Economics等国际知名学术期刊上发表论文20余篇。