Description
Taylor & Francis Ltd Introduction To Spatial Econometrics 2009 Edition by James LeSage, Robert Kelley Pace
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 It encompasses many recent advances in spatial econometric models-including some previously unpublished results. IntroductionSpatial dependence The spatial autoregressive process An illustration of spatial spillovers The role of spatial econometric models The plan of the textMotivating and Interpreting Spatial Econometric Models A time-dependence motivation An omitted variables motivation A spatial heterogeneity motivation An externalities-based motivation A model uncertainty motivation Spatial autoregressive regression models Interpreting parameter estimatesMaximum Likelihood Estimation Model estimationEstimates of dispersion for the parametersOmitted variables with spatial dependenceAn applied exampleLog-Determinants and Spatial WeightsDeterminants and transformations Basic determinant computation Determinants of spatial systemsMonte Carlo approximation of the log-determinant Chebyshev approximation Extrapolation Determinant bounds Inverses and other functions Expressions for interpretation of spatial models Closed-form solutions for single parameter spatial models Forming spatial weightsBayesian Spatial Econometric Models Bayesian methodology Conventional Bayesian treatment of the SAR modelMCMC estimation of Bayesian spatial modelsThe MCMC algorithm An applied illustration Uses for Bayesian spatial modelsModel Comparison Comparison of spatial and non-spatial models An applied example of model comparisonBayesian model comparison Chapter appendixSpatiotemporal and Spatial Models Spatiotemporal partial adjustment model Relation between spatiotemporal and SAR models Relation between spatiotemporal and SEM models Covariance matrices Spatial econometric and statistical models Patterns of temporal and spatial dependenceSpatial Econometric Interaction ModelsInterregional flows in a spatial regression context Maximum likelihood and Bayesian estimation Application of the spatial econometric interaction model Extending the spatial econometric interaction modelMatrix Exponential Spatial Models The MESS model Spatial error models using MESS A Bayesian version of the model Extensions of the modelFractional differencingLimited Dependent Variable Spatial Models Bayesian latent variable treatmentThe ordered spatial probit model Spatial Tobit models The multinomial spatial probit modelAn applied illustration of spatial MNP Spatially structured effects probit modelsReferencesA summary appears at the end of each chapter.