" Spatial Econometrics: Methods and Models " by Luc Anselin is a comprehensive treatise on the econometric analysis of spatial processes. The book is divided into three main parts: Foundations for the Econometric Analysis of Spatial Processes, Estimation and Hypothesis Testing, and Model Validation. In the first part, Anselin introduces the scope of spatial econometrics, the formal expression of spatial effects, and a taxonomy of spatial econometric models. He discusses spatial stochastic processes, their properties, and asymptotic approaches. The second part focuses on estimation and hypothesis testing, covering maximum likelihood estimation, alternative approaches such as instrumental variables and Bayesian methods, and robust approaches like bootstrapping. It also addresses spatial dependence in regression error terms and spatial heterogeneity. The third part delves into model validation, including specification tests, model selection criteria, and practical implications. The book includes numerous empirical examples and illustrations to support the theoretical discussions. Anselin's approach is model-driven, emphasizing the relevance of spatial effects on model specification, estimation, and inference. The book aims to bridge the gap between standard econometrics and the unique challenges posed by spatial data, making it a valuable resource for researchers and practitioners in regional science, spatial analysis, and applied econometrics." Spatial Econometrics: Methods and Models " by Luc Anselin is a comprehensive treatise on the econometric analysis of spatial processes. The book is divided into three main parts: Foundations for the Econometric Analysis of Spatial Processes, Estimation and Hypothesis Testing, and Model Validation. In the first part, Anselin introduces the scope of spatial econometrics, the formal expression of spatial effects, and a taxonomy of spatial econometric models. He discusses spatial stochastic processes, their properties, and asymptotic approaches. The second part focuses on estimation and hypothesis testing, covering maximum likelihood estimation, alternative approaches such as instrumental variables and Bayesian methods, and robust approaches like bootstrapping. It also addresses spatial dependence in regression error terms and spatial heterogeneity. The third part delves into model validation, including specification tests, model selection criteria, and practical implications. The book includes numerous empirical examples and illustrations to support the theoretical discussions. Anselin's approach is model-driven, emphasizing the relevance of spatial effects on model specification, estimation, and inference. The book aims to bridge the gap between standard econometrics and the unique challenges posed by spatial data, making it a valuable resource for researchers and practitioners in regional science, spatial analysis, and applied econometrics.