SPATIAL ECONOMETRICS
SPATIAL AUTOREGRESSIVE MODELS
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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 larg...
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 models. Recent econometric developments on intertemporal spatial models with rational expectations and 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.
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