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Record Nr. |
UNINA9910903793203321 |
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Autore |
Arbia Giuseppe |
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Titolo |
A Primer for Spatial Econometrics : With Applications in R, STATA and Python |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing AG, , 2024 |
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©2024 |
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ISBN |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (250 pages) |
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Collana |
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Palgrave Texts in Econometrics Series |
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Soggetti |
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Econometric models |
Econometrics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Foreword to the First Edition -- Preface to the Second Edition -- Preface and Acknowledgments to the First Edition -- Contents -- List of Figures -- 1 The Classical Linear Regression Model -- 1.1 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 Spatial 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 |
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Sommario/riassunto |
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This second edition of 'A Primer for Spatial Econometrics with Applications in R, STATA, and Python' by Giuseppe Arbia offers an updated and comprehensive introduction to spatial econometrics, a |
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dynamic and evolving field. The book expands on the theoretical and practical aspects of spatial econometric models, including linear, non-linear, and Bayesian approaches. It introduces new topics such as the spatial Durbin and Tobit models and updates its content with modern software tools, incorporating R, STATA, and Python to cater to a broader audience. The text is designed for researchers and students in econometrics and related fields, providing practical examples and tutorials to facilitate the application of spatial econometrics techniques across various disciplines. |
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