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A Primer for Spatial Econometrics, Second Edition | |||||||||||
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| Comment from the Stata technical group Giuseppe Arbia's book, A Primer for Spatial Econometrics, is written for anyone wanting to learn and apply spatial econometrics in empirical research. The book is suitable for beginners because only undergraduate econometrics training is assumed. Those with more experience will also find it beneficial because it outlines the recent developments in spatial econometrics research. The book is divided into five chapters. The first three chapters introduce readers to the foundation of spatial econometrics: classical linear regression, the spatial weighting matrix, and spatial linear autoregressive models. The fourth chapter outlines recent developments in spatial econometrics research, such as heterogeneity, spatial models for binary variables, the spatial panel-data model, and the endogenous spatial weighting matrix. Finally, the fifth chapter introduces alternative spatial model specifications for big datasets. A good feature of this book is that there are sections dedicated to computer codes in Stata, R, and Python for each different spatial econometrics model. This allows readers to get practical experience to deepen their understanding of spatial econometrics theory. In summary, A Primer for Spatial Econometrics, is a great introduction for researchers and students wanting to learn spatial econometrics from theoretical, empirical, and computational points of view. | |||||||||||
Table of contents View table of contents >> I The Classical Linear Regression Model1.1 The Basic Linear Regression Model 1.2 Non-sphericity of the Disturbances 1.3 Endogeneity 1.4 Computer Codes 1.4.1 Running a Regression with R 1.4.2 Running a Regression with Stata 1.4.3 Running a Regression with Python References 2 Some Important Spatial Definitions 2.1 The Spatial Weight Matrix W and the Definition of Spatial Lag 2.2 Testing Sptial Autocorrelation Among OLS Residuals Without an Explicit Alternative Hypothesis 2.3 Computer Codes: R 2.3.1 Creating and Managing W Matrices 2.3.2 Calculating Moran's I Spatial Correlation 2.3.3 Some Useful Spatial R Databases 2.4 Computer Codes: Stata 2.4.1 Creating and Managing W Matrices 2.4.2 Calculating Moran's I Spatial Correlation 2.4.3 Some Useful Databases to Be Used in Stata 2.5 Computer Codes: Python 2.5.1 Creating and Managing W Matrices 2.5.2 Calculating Moran's I Spatial Correlation 2.5.3 Some Useful PySAL Databases References 3 Spatial Linear Regression Models 3.1 Generalities 3.2 Pure Spatial Autoregression 3.3 The Spatial Lag of X Model (SLX) 3.4 The Spatial Error Model (SEM) 3.4.1 Introduction 3.4.2 Maximum Likelihood Estimator 3.4.3 Feasible GLS 3.5 The Spatial Lag Model (SLM) 3.5.1 Generalities 3.5.2 Maximum Likelihood Estimator 3.5.3 Two Stages Least Squares Estimators 3.6 The Spatial Durbin Model 3.7 The General SARAR(1,1) Model3.7.1 Generalities 3.7.2 Maximum Likelihood Estimator 3.7.3 The Generalized Spatial Two Stages Least Squares (GS2SLS) 3.7.4 The Lee Fully Efficient Estimators 3.8 Testing Spatial Autocorrelation Among the Residuals with an Explicit Alternative Hypothesis 3.8.1 Testing Spatial Autocorrelation Among the Residuals Using SEM or SLM as Alternatives 3.8.2 Testing Spatial Autocorrelation Among the Residuals Using a Spatial Model as an Alternative: The Modified Moran's I Test 3.9 Interpretation of the Parameters in Spatial Econometric Models 3.11 Estimating Linear Spatial Models with Stata 3.12 Estimating Linear Spatial Models with Python References to the Chapter 4 Further Topics in Spatial Econometrics 4.1 Heteroscedastic Innovations4.1.1 Generalities 4.1.2 The SARAR Model with Heteroscedastic Disturbances 4.1.3 Spatial HAC Estimators 4.2 Spatial Models for Binary Response Variables4.2.1 Introduction 4.2.2 The A-Spatial Logit and Probit Models 4.2.3 The Spatial Logit and Probit Models 4.2.4 The Spatial Tobit Model 4.2.5 Further Spatial Discrete Choice Models 4.3 Spatial Panel Data Models (Written by Giovanni Millo) 4.3.1 Generalities 4.3.2 Unobserved Heterogeneity and Individual Effects 4.3.3 Spatial Panel Models with Random Effects 4.3.4 Spatial Panel Models with Fixed Effects 4.3.5 Estimation 4.3.6 Further Modeling Frameworks in Spatial Panel Data Modeling 4.4 Non-stationary Spatial Econometric Models 4.4.1 Generalities 4.4.2 Geographically Weighted Regression 4.4.3 Further Developments 4.5 Bayesian Spatial Models 4.6 Stochastic and Endogenous Weighting Matrices 4.6.1 Stochastic Weight Matrices 4.6.2 Endogenous Weight Matrices 4.7 Computer Codes 4.7.1 Estimating Heteroscedastic Linear Spatial Models with R 4.7.2 Estimating Heteroscedastic Linear Spatial Models with Stata 4.7.3 Estimating Heteroscedastic Linear Spatial Models with Python 4.7.4 Estimating Spatial Probit/Logit/Tobit Models with R 4.7.5 Estimating Spatial Probit/Logit/Tobit Models with Stata 4.7.6 Estimating Spatial Panel Models with R 4.7.7 Estimating Spatial Panel Models with Stata 4.7.8 Estimating Spatial Panel Models with Python 4.7.9 Estimating Geographically Weighted Regression Models with R 4.7.10 Estimating Geographically Weighted Regression Models with Stata 4.7.11 Estimating Geographically Weighted Regression Models with Python 4.7.12 Estimating Bayesian Spatial Econometric Models with R References to the Chapter 5 Alternative Model Specifications for Big Datasets 5.1 Introduction: Spatial Econometrics and the Big Data Revolution 5.2 The MESS Specification 5.2.1 A MESS Spatial Lag Specification 5.2.2 A MESS Spatial Error Specification and Further Extensions 5.3 The Unilateral Approximation Approach 5.3.1 The Importance of Asymmetries and Anisotropies in Spatial Econometrics 5.3.2 Testing Isotropy in Spatial Lag Models 5.3.3 Inference for a Unilateral Spatial Lag Model 5.4 A Composite Likelihood Approach 5.4.1 Generalities 5.4.2 A Bivariate Marginal Likelihood Approach to Spatial Error Model Estimation 5.5 Handling the Second "V" of Big Data (Velocity) in Spatial Regressions 5.6 R Codes References 6 Conclusions: What's next? References Index | |||||||||||
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