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 It encompasses many recent advances in spatial econometric models-including some previously unpublished results.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 ∙ 勒沙傑（James LeSage），德剋薩斯州立大學（聖馬科斯）麥考伊工商管理學院金融經濟係教授，經濟學博士。勒沙傑教授是區域科學領域最有影響力的學者之一，也是該領域著作被引用次數最多的學者之一，在Journal of Econometrics, Journal of Applied Econometrics, Journal of Regional Science等國際知名學術期刊上發錶論文60餘篇，並為Journal of Political Economy, Review of Economics and Statistics等眾多知名學術期刊審稿人。

凱利 ∙ 佩斯（R.Kelley Pace）是路易斯安那州立大學金融係教授，房地産學博士。佩斯教授的主要研究領域是房地産金融，已在Journal of Real Estate Finance and Economics, Real Estate Economics等國際知名學術期刊上發錶論文20餘篇。