A primer for spatial econometrics: with applications in R
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Basingstoke [u.a.]
Palgrave Macmillan
[2024]
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| Ausgabe: | Second edition |
| Schriftenreihe: | Palgrave texts in econometrics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xix, 234 Seiten Illustrationen 23 cm |
| ISBN: | 9783031571817 |
Internformat
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| 245 | 1 | 0 | |a A primer for spatial econometrics |b with applications in R |c Giuseppe Arbia |
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Datensatz im Suchindex
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|---|---|
| adam_text |
Contents 1 2 The Classical Linear Regression Model. 1 1.1 1.2 1.3 1.4 The Basic Linear Regression Model. Non-sphericity of the Disturbances. Endogeneity . 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 . 1 Il 15 19 19 21 22 29 Some Important Spatial Definitions . 31 The Spatial Weight Matrix W and the Definition of Spatial Lag. 31 2.2 Testing Spatial Autocorrelation AmongOLS Residuals Without an Explicit AlternativeHypothesis . 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 . 36 41 41 48 49 51 51 53 53 54 54 56 56 59 2.1
XVI 3 Contents Spatial Linear Regression Models. 61 Generalities . Pure Spatial Autoregression . The Spatial Lag of X Model (SLX) . 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) Model . 3.7.1 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. 93 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 Models as an Alternative: The Modified Moran’s I Test. 95 3.9 Interpretation of the Parameters in Spatial Econometric Models . 97 3.10 Estimating Linear Spatial Models with R . 3.11 Estimating Linear Spatial Models with STATA . 3.12 Estimating Linear Spatial Models with Python. References to the Chapter . 61 62 64 67 67 68 70 76 76 77 78 83 86 86 88 3.1 3.2 3.3 3.4 4 4.2 94 101 103 105 109 Ill Heteroscedastic Innovations. 112 4.1.1 Generalities . 112 4.1.2 The SARAR Model withHeteroscedastic Disturbances . 112 4.1.3 Spatial НАС Estimators. 116 Spatial Models for Binary Response Variables . 122 4.2.1
Introduction. 122 4.2.2 The A-Spatial Logit and Probit Models . 123 Further Topics in Spatial Econometrics . 4.1 89 91
Contents 4.3 4.4 4.5 4.6 4.7 xvii 4.2.3 The Spatial Logit and Probit Models. 4.2.4 The Spatial Tobit Model . 4.2.5 Further Spatial Discrete Choice Models . 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 . Non-stationary Spatial Econometric Models. 4.4.1 Generalities . 4.4.2 Geographically Weighted Regression . 4.4.3 Further Developments . Bayesian Spatial Models . Stochastic and Endogenous Weighting Matrices . 4.6.1 Stochastic Weight Matrices. 4.6.2 Endogenous Weight Matrices. Computer Codes
. 4.7.1 Estimating Heteroscedastic Linear Spatial Models with R. 172 4.7.2 Estimating Heteroscedastic Linear Spatial Models with STATA. 173 4.7.3 Estimating Heteroscedastic Linear Spatial Models with Python . 173 4.7.4 Estimating Spatial Probit/Logit/Tobit Models with R . 4.7.5 Estimating Spatial Probit/Logit/Tobit Models with STATA. 175 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 127 136 138 140 140 141 143 144 145 151 154 154 156 159 161 166 166 170 172 174 176 178 179 . 181 4.7.10 Estimating Geographically Weighted Regression Models with STATA. 182 4.7.11 Estimating Geographically Weighted Regression Models with Python. 182 4.7.12 Estimating Bayesian Spatial Econometric Models with R. 183 References to the Chapter . 188
xvüi Contents 5 Alternative Model Specifications for Big Datasets. 5.1 Introduction: Spatial Econometrics and the Big Data Revolution . 193 5.2 The MESS Specification . 5.2.1 A MESS Spatial LagSpecification . 5.2.2 A MESS Spatial Error Specification and Further Extensions . 200 5.3 The Unilateral Approximation Approach . 5.3.1 The Importance of Asymmetries and Anisotropies in Spatial Econometrics. 204 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 . 213 5.5 Handling the Second “V” of Big Data (Velocity) in Spatial Regressions . 218 5.6 R Codes. References . 193 195 195 204 206 209 213
213 219 223 Conclusions: What’sNext?. References . 225 227 Index. 229 6 |
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| institution | BVB |
| isbn | 9783031571817 |
| language | English |
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| physical | xix, 234 Seiten Illustrationen 23 cm |
| publishDate | 2024 |
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| publisher | Palgrave Macmillan |
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| spelling | Arbia, Giuseppe 1958- Verfasser (DE-588)131479857 aut A primer for spatial econometrics with applications in R Giuseppe Arbia Second edition Basingstoke [u.a.] Palgrave Macmillan [2024] xix, 234 Seiten Illustrationen 23 cm txt rdacontent n rdamedia nc rdacarrier Palgrave texts in econometrics Regionalanalyse (DE-588)4299244-8 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Ökonometrie (DE-588)4132280-0 s Regionalanalyse (DE-588)4299244-8 s b DE-604 Erscheint auch als Online-Ausgabe 978-3-031-57182-4 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=035434201&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Arbia, Giuseppe 1958- A primer for spatial econometrics with applications in R Regionalanalyse (DE-588)4299244-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4299244-8 (DE-588)4132280-0 (DE-588)4151278-9 |
| title | A primer for spatial econometrics with applications in R |
| title_auth | A primer for spatial econometrics with applications in R |
| title_exact_search | A primer for spatial econometrics with applications in R |
| title_full | A primer for spatial econometrics with applications in R Giuseppe Arbia |
| title_fullStr | A primer for spatial econometrics with applications in R Giuseppe Arbia |
| title_full_unstemmed | A primer for spatial econometrics with applications in R Giuseppe Arbia |
| title_short | A primer for spatial econometrics |
| title_sort | a primer for spatial econometrics with applications in r |
| title_sub | with applications in R |
| topic | Regionalanalyse (DE-588)4299244-8 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Regionalanalyse Ökonometrie Einführung |
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