This is the most recently developed book in Spatial Econometrics which cover important models and estimation methods. Its coverage is rather broad, and some of the topics covered have only been developed in the recent econometric literature in spatial econometrics.The book summarizes our devoted efforts on spatial econometrics that represent joint contributions with former PhD advisees from the Ohio State University in Columbus, Ohio, USA.The coverage is comprehensive and there are a total of sixteen chapters from basic statistics and statistical theory of linear-quadratic forms, law of large numbers (LLN) and central limit theory (CLT) on martingales to nonlinear spatial mixing and spatial near-epoch dependence theories, which can justify the statistic inferences for various spatial models and their estimation. New estimation and testing approaches in empirical likelihood and general empirical likelihood, and Bootstrapping are presented. Model selection is also discussed in this book. In addition to the popular spatial autoregressive models, there are chapters on multivariate SAR models, simultaneous SAR models, and panel dynamic spatial model models. Recent econometric developments on intertemporal spatial models with rational expectations and on flows data in trade theory will also be included. In terms of statistics, classical estimation, testing and inference are the main concerns, and we provide classical inference for the justification of Bayesian simulation approaches.Key FeaturesThis book presents the most recent developments on spatial econometrics on model specifications, estimation methods and testing proceduresThe quasi-likelihood (QL), generalized method of moments (GMM), and empirical likelihood (EL) are presented for estimationLinear-quadratic statistics characterize asymptotic properties of estimators for linear SAR models. Spatial mixing and spatial near epoch dependence theories are for nonlinear spatial econometric modelsClassical estimation methods are the main concern. But the justification of Bayesian estimation methods by classification statistical theories is providedThe models discussed are spatial autoregressive models. Such models can be developed for cross sectional data, panel data, as well as intertemporal optimized differential games

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