LEADER 03394nam 22004573 450 001 9910903793203321 005 20241104084505.0 010 $a3-031-57182-7 035 $a(CKB)36443140700041 035 $a(MiAaPQ)EBC31747143 035 $a(Au-PeEL)EBL31747143 035 $a(Exl-AI)31747143 035 $a(EXLCZ)9936443140700041 100 $a20241104d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Primer for Spatial Econometrics $eWith Applications in R, STATA and Python 205 $a2nd ed. 210 1$aCham :$cSpringer International Publishing AG,$d2024. 210 4$d©2024. 215 $a1 online resource (250 pages) 225 1 $aPalgrave Texts in Econometrics Series 311 $a3-031-57181-9 327 $aForeword 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$7Generated by AI. 330 $aThis 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 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.$7Generated by AI. 410 0$aPalgrave Texts in Econometrics Series 606 $aEconometric models$7Generated by AI 606 $aEconometrics$7Generated by AI 615 0$aEconometric models. 615 0$aEconometrics. 700 $aArbia$b Giuseppe$089272 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910903793203321 996 $aA Primer for Spatial Econometrics$94272910 997 $aUNINA