Verfügbarkeit: Modern spatial econometrics in practice

Modern spatial econometrics in practice: a guide to GeoDa, GeoDaSpace and PySAL

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Bibliographische Detailangaben
Beteiligte Personen:	Anselin, Luc (VerfasserIn), Rey, Sergio J. (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	2014
Schlagwörter:	Econometric models Space in economics Spatial analysis (Statistics) Ökonometrisches Modell Räumliche Statistik
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028152249&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Includes bibliographical references and index
Umfang:	xx, 368 pages illustrations (some color) 23 cm
ISBN:	9780986342103

Internformat

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Datensatz im Suchindex

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adam_text	Contents List of Figures List of Tables Preface 1 Introduction 1 1 Notation, Models and Methods 0008 12 Software ee ees 12 1 GeoDa 2 ee ee ee 1211 Obtaining and Installing GeoDa 122 PySAL spreg ee ees 1221 Obtaining and Installing PySAL spreg 123 GeoDaSpace 6 ee es 1 3 Example Data Sets 2 ee 131 The Baltimore House Price Sample Data Set 132 The U S County Homicide Sample Data Set 1 4 Organization of the Book 1 ee ee ee I Preliminaries 2 Getting Started 2 1 The GeoDa Interface 2 ee ee eee 211 Model Specification in the Regression Interface 2 2 The GeoDaSpace Main GUI 0 0002 0Ge 221 Regression Model Specification in GeoDaSpace 222 GeoDaSpace Preferences Settings 223 Saving and Loading Models 0-2055 2 3 PySAL and spreg Basics 1 ee es 231 The PySAL Logic 1 es 232 Loading Variables into PySAL 6 -- ee eee 3 Spatial Weights: Contiguity 3 1 Basic Principles 2 0 ee 311 Rook and Queen Contiguity 0 ee ees 312 Block Weights 2 6 ce ee ees ü CONTENTS 313 Higher Order Contiguity 2:0 m om 37 314 Transformation of Weights 37 3141 Row-Standardization , 38 3142 Double Standardization , 38 3143 Variance Stabilizing 200 00, 38 315 Characteristics of Weights 0 , 38 3151 Isolates 2 ee eee eee 39 316 Spatially Lagged Variables 2 cc ce 40 317 Weights File Formats 2 S come ren 4 3 2 Contiguity Weights inGeoDa 1 0 ee eee 42 321 The Weights Interface 2 0 cee eee ee 42 322 Creating Contiguity Weights 43 3221 First Order Contiguity BB 3222 Higher Order Contiguity 44 323 Weights Characteristics 0 00000008 46 324 Constructing Spatially Lagged Variables 46 3 3 Contiguity Weights in GeoDaSpace 000 47 331 The Weights Interface 000 47 332 Creating Model Weights 00 48 333 Reading Contiguity Weights 0 48 334 Weights Properties © 0 eee ee 50 3341 Weights Transformations 50 3342 Visualizing Weights Properties 51 335 Creating Spatially Lagged Variables 52 3 4 Contiguity Weights in PySAL 2 oo none 54 341 Spatial Weights Object 22: oo onen 54 3411 The Spatial Weights ClassW 54 3412 Attributes of a Spatial Weights Object 58 3413 The Sparse Spatial Weights Class WSP 65 3414 Attributes of a Sparse Spatial Weights Object 65 342 Creating Contiguity Weights from a Shapefile 66 3421 Queen Contiguity Weights 66 3422 Rook Contiguity Weights 68 343 Creating Weights for a Regular Lattice Structure 68 344 Block Weights 0 20 none 69 345 Higher Order Contiguity 2 2 mom 70 346 Reading Weights Files 2 2 co mon 71 347 Writing and Converting Weights Files 72 348 Creating Spatially Lagged Variables 73 4 Spatial Weights: Distance 75 4 1 Basic Principles 2 ee ees 75 411 Distance Metrics 220 eee 75 4111 Great Circle Distance 0 00 76 412 Distance Band Weights 2 co 22 une 76 4121 Inverse Distance Functions 77 413 k-Nearest Neighbor Weights 222222000 78 CONTENTS tii 414 Kernel Weights 0 0 000 eee 78 4 2 Distance Based Weights inGeoDa 79 421 Distance Band 2 ee eee 79 42 1 1 Great Circle Distance 80 422 k-Nearest Neighbors 0000 cease 81 4 3 Distance Based Weights in GeoDaSpace 81 431 Selecting a Distance Metric 222 ee ee 82 432 k-Nearest Neighbor Weights -0000 5 83 433 Binary Distance Band Weights 83 434 Inverse Distance Weights 0000- 84 435 Kernel Weights 2 rennen 85 4 4 Distance Based Weights in PySAL -2000- 85 441 Creating Distance Weights from a Shapefile 86 4411 Binary Weights for k-Nearest Neighbors 86 4412 Binary Weights from a Distance Band 87 4413 Inverse Distance Weights 89 4414 Great Circle Distance 90 442 Creating Kernel Weights 2000008 91 II Non-Spatial Models 95 5 Ordinary Least Squares (OLS) 97 5 1 Basic Prineiples -- 22cm eeserrennenenn 97 511 Regularity Conditions 0 eee eee 97 512 Estimator and Inference 1 0 eee ee ees 98 513 Robust Inference - -- 02 2 e eee eee 99 514 Measures of Fit 0 2 ee ee ee eee 100 515 Non-Spatial Diagnostics 0 6 eee eee 101 5151 Multicollinearity 2 2 ee ee es 102 5152 Normality 6 0 ee ee ee ees 102 5153 Heteroskedasticity 2 0 eee eee 103 516 Diagnostics for Spatial Dependence - -- - 104 5161 Lagrange Multiplier Test against Spatial Lag and Spatial Error 2 ee ee es 104 5162 Lagrange Multiplier Test against Higher Order Spatial Autocorrelation © 66e eee 106 5163 Robust forms of the Lagrange Multiplier tests 107 5164 Moran’sI 2 eee eee ee ees 107 517 Specification Search 6 ee ee es 109 52 OLS inthe GUI 2 ee et es 111 521 Model Specification 2 ee ee eee 111 522 Basic OLS Regression Results - rc 113 523 Non-Spatial Regression Diagnostics -+---+- 116 5231 White Test Statistic 0 -- ee eee 117 524 Diagnostics for Spatial Effects - -- ++ seers 118 5241 Specifying the Weights File --++-+:: 118 v CONTENTS 5242 Lagrange Multiplier Statistics 2 00, 119 5243 Moran’sIT 0 ee eee ee eee 121 5244 Spatial Specification Search , 192 525 Predicted Values and Residuals : v2 2220 123 5251 GeodDa 20er ren 123 5252 GeoDaSpace comme 124 526 Robust Coefficient Variance Estimation 124 5261 White Standard Errors 125 5262 Heteroskedastic and Autocorrelation Consistent Standard Errors (HAC) 127 5263 Comparison of Standard Errors 128 5 3 OLS in PySAL spreg 6 ee es 129 531 Basic Regression Setup : ee ee ee 129 532 The OLS Command 2 00 eee eee 130 533 The OLS Object 1 es 131 534 OLS with White Test 0 0 cee ees 136 535 OLS with Spatial Diagnostics 1 2 eee ee 136 536 OLS with White Standard Errors -- 136 537 OLS with HAC Standard Errors - --55 137 538 Accessing Individual Functions - 0-+-- 137 6 Two Stage Least Squares (2SLS) 139 6 1 Basic Prineiples ee es 139 611 Endogeneity and Instruments 1 +++ sees 139 612 The 2SLS Estimator 2 eee ee eee es 140 613 Inference 2 1 ee ee tes 141 614 Diagnostics for Spatial Autocorrelation ---+: 142 615 Robust Coefficient Variance Estimation -: 143 6 2 WLS inthe GUI 6 ee es 144 621 Model Specification 1 ee ee eee 144 622 Basic Estimation Results 6 6 eee ees 145 623 Diagnostics for Spatial Effects 2 +s eres 146 624 Robust Coefficient Variance Estimates 147 6241 White Variance Estimate --- +e ee 148 6242 HAC Variance Estimate - --- +++: 148 6243 Comparing Standard Errors for 2SLS_ -- 149 6 3 2SLS in PySAL spreg «6 ee ee eee ts 149 631 Basic Regression Setup -- eee eee ees 149 632 The TSLS Command - - eee ee ees 151 6 33 The TSLS Object 2 0 eee ee ee es 152 634 The Two Stages of 2SLS - ee eee ts 154 6 35 TSLS with Spatial Diagnostics een 155 636 TSLS with White Standard Errors 6s esses 155 637 TSLS with HAC Standard Errors - +--+ sec: 155 CONTENTS v III Spatial Dependence 157 7 Spatial Lag Model, Spatial Two Stage Least Squares (S2SLS)159 7 1 Basic Principles 2 move rn 159 711 Model Specification 2 22 cc onen 159 712 Endogeneity and Instruments 160 7121 Additional Endogenous Variables 161 713 Estimation and Inference 04 162 714 Coefficient Interpretation and the Spatial Multiplier 164 715 Predicted Values and Residuals 165 716 Diagnostics for Spatial Effects 166 717 Robust Coefficient Variance Estimation 167 7 2 S2SLS inthe GUI 2 ee eee 167 721 Model Specification 20000 000 eae 167 7211 Instruments Preference Panel Settings 168 722 Basic Estimation Results 0 00004 ee 169 72 2 1 Goodness-offt 2 2 2220 een 171 7222 Using Higher Order Contiguity 171 7223 Comparison of Estimates 171 723 S2SLS, Diagnostics for Spatial Effects 173 724 Robust Variance Estimates 04 174 7241 Comparison of Standard Errors 175 725 S2SLS, Endogenous Variables 22222200 176 7251 S2SLS, Instrument Options 178 7 3 S2SLS in PySAL spreg 2 ees 180 731 Basic Regression Setup --- 2222er 181 732 The GMLag Command 0000 181 733 The GM_Lag Object 2 ee eee 182 7331 Direct, Indirect and Total Effects 184 734 GM_Lag with Spatial Diagnostics 185 7 35 GM_Lag with White Standard Errors 185 736 GM_Lag with HAC Standard Errors 186 737 GM_Lag with Endogenous Variables 186 8 Spatial Lag Model, Maximum Likelihood (ML) 187 8 1 Basic Principles 6 es 187 811 The Likelihood Function 1 0 eee ees 187 8111 The Concentrated Likelihood Function 189 8112 Estimation Steps 20 020 eee eee 190 812 The Asymptotic Variance Matrix -5 190 813 Measures of Fit 2 ees 191 814 Diagnostics es 192 8141 Likelihood Ratio Test -20--5 192 8142 Heteroskedasticity - eee eee eee 192 815 Computational Considerations © 1 006s eee 192 8 2 Spatial Lag ML inthe GUI 0 0 cee eee 194 821 Model Specification 0 ee es 194 vi CONTENTS 822 ML, Basic Estimation Results 0 195 823 ML Preference Panel Settings 00 198 Spatial Lag ML in PySAL spreg 0 - ee eee eae 200 831 Basic Regression Setup 2 ee eee ee eee 200 832 The MLLag Command +e eevee 200 833 The MLLag Object © 6 ee eee eee 201 834 ML_Lag Estimation Options 6 6 256 202 8341 Full Method 2 ee ee ee 202 83 42 Ord Method 0 000 eee eee 203 9 Spatial Error Model, General Method of Moments (GMM) 205 Basic Prineiples -- ee ees 205 911 Model Specification 6 ee ee ees 205 9111 Spatial Durbin Model rer 206 9112 SAR Errors with Endogenous Variables in the Regression eee ees 207 912 Spatially Weighted Least Squares +e: 208 9121 Purely Exogenous Explanatory Variables 208 9122 Exogenous and Endogenous Explanatory Vari- ables 2 ee ee es 210 913 Generalized Moments (GM) Estimation + - 211 914 Generalized Method of Moments (GMM) --- 212 9141 Heteroskedastic Errors 1 ee +e eres 214 9142 Homoskedastic Errors 2 +e sees 215 915 Inference 1 - eee ee es 216 9151 Heteroskedasticity, Exogenous and Endogenous Explanatory Variables 2 0 eee eee 217 9152 Heteroskedasticity, Only Exogenous Variables 218 9153 Homoskedasticity, Exogenous and Endogenous Explanatory Variables 1 -+ e+e 218 9154 Homoskedasticity, Only Exogenous Variables 219 916 Residuals and Measures of Fit screen 220 GM/GMM Error in the GUL es 220 921 Model Specification 6 ee ee ees 220 9211 GM vs GMM Estimation Method - - 221 922 GM 2 ccc ee eee 222 9221 GM with Endogeneity -005: 223 923 GMM with Heteroskedasticity 6-62 20 ees 225 9231 Extra Step in the Estimation of A -- 226 9232 Iterated Estimation 0 0000 ee 227 9233 GMM with Heteroskedasticity and Endogeneity 228 924 GMM with Homoskedasticity 1 6 ee ee ees 228 9241 GMM with Homoskedasticity and Endogeneity 229 925 Comparison of Estimates 2 eee eee ees 230 GM/GMM Error in PySAL spreg +--+ ss eee eters 231 931 Basic Regression Setup rer ernennen 231 9311 Exogenous Variables Only -- - +--+: 232 CONTENTS vü 9312 Exogenous and Endogenous Variables 232 9313 Spatial Weights Object 0 232 932 The GM_Error Command 025 233 9321 The GM_Error Object 233 933 The GM_EndogError Command 234 9331 The GM_Endog_Error Object 235 934 The GM_Error Het Command , 236 9341 The GM_ErrorHet Object 237 9342 The stepic Option 000 % 238 93 43 The max_iter Option 238 935 The GM Endog-Error Het Command 238 9351 The GM_Endog_ErrorHet Object 239 9 36 The GM Error Hom Command 239 9361 The GM_ErrorHom Object 240 9362 Selection of the A; Matrix 240 937 The GM-Endog-ErrorHom Command 242 9371 The GM_Endog_Error Hom Object 242 10 Spatial Error Model, Maximum Likelihood (ML) 245 10 1 Basic Principles ee ee 245 10 1 1 The Likelihood Function 222er 245 10 111 The Concentrated Likelihood Function 247 10 1 2 The Asymptotic Variance Matrix 05- 247 10 1 8 Measures of Fit 2 2 eee eee 248 10 1 4 Diagnostics © ee eee 248 10 141 Likelihood Ratio Test 248 10 142 Heteroskedasticity 0 ee ee eee 248 10 1 5 Computational Considerations 4 248 10 2 Spatial Error MLintheGUl ee eee 248 10 2 1 Model Specification 1 ee ee es 248 10 2 2 Basic Estimation Results 000 05 eee 249 10 2 3 ML — Predicted Values and Residuals 250 10 3 Spatial Error ML in PySAL spreg 6 eee eens 253 10 3 1 Basic Regression Setup 2 eee eee ees 253 10 3 2 The ML Error Command - 002+ eee 253 10 3 3 The ML_Error Object 2 2 ee ee ee ees 254 10 3 4 ML_Error Estimation Options 6 eee ee ees 255 10 341 Full Method 1 0 ee eee eee 255 10 342 Ord Method 2 eee ees 255 10 343 Timing Comparisons - +--+ sees 256 11 Models with Lag and Error, Generalized S2SLS (GS2SLS) 257 11 1 Basic Principles 2 ee es 257 11 1 1 Model Specification ee ees 257 11 111 Reduced Form and Spatial Multipliers 259 11 112 Models Containing Endogenous Explanatory Vari- ables 2 ee ee eee 259 viii CONTENTS 11 1 2 Generalized Spatial Two Stage Least Squares Estimation (GS2SLS) 26 ee 259 11 1 3 Residuals and Measures of Fit -2 0 260 11 2 Combo Model in the GUI 1 ee ee eee 261 11 2 1 Model Specification 6 ee ee ee eee 261 11 211 Exogenous and Endogenous Explanatory Vari- ables ee es 263 11 2 2 GM Estimation of the Combo Model 264 11 2 3 GMM Estimation of the Combo Model 266 11 231 Exogenous Explanatory Variables 266 11 232 Exogenous and Endogenous Explanatory Vari- ables 2 0 ee 268 11 233 Comparison of Estimates 2 eae 270 11 3 Combo Model in PySAL spreg 2 6 ee eee ees 271 11 3 1 Basic Regression Setup 6 ee eee 272 11 311 Exogenous explanatory variables only 272 11 312 Exogenous and endogenous explanatory variables272 11 313 Spatial weights object 222000 272 11 3 2 The GM-Combo Command 005 273 11 321 The GM Combo object 274 11 322 Options 220 02 02 000 276 11 3 3 The GM-ComboHom Command 276 11 331 The GM_-Combo-Hom object 278 11 332 Options © 000 00 cee eee eee 279 11 3 4 The GM_Combo_Het Command - 279 11 341 The GM_Combo Het object 279 11 342 Options ee es 280 IV Spatial Heterogeneity 281 12 Spatial Regimes, Non-Spatial Models 283 12 1 Basic Principles 2 eee ne 283 12 1 1 Model Specification © 2 2 nm can 283 12 1 2 Groupwise Heteroskedasticity 0 0 284 12 1 3 Optimal Weighted GMM 0002 285 12 1 4 Chow Test on Coefficient Stability 285 12 1 5 Spatial Weights with Spatial Regimes 287 12 2 Spatial Regimes in the GUI 0004 288 12 2 1 Model Specification 2 0 ee ee 288 12 2 2 OLS with Spatial Regimes 004 290 12 221 Error by Regimes 004 290 12 222 Homoskedasiticity 2 22 cc een 294 12 2 3 25LS with Spatial Regimes 2 cc 296 12 231 Error by Regimes 2 2 cc onen 296 12 232 Homoskedasiticity 200 00 000 298 12 3 Spatial Regimes in PySAL spreg 0 0 00 ae 300 CONTENTS ix 12 3 1 Basic Regime Regression Setup 05 300 12 3 2 The OLS_Regimes Command 301 12 321 Preliminaries 200 00020 301 12 322 Error by Regime 0000- 302 12 323 Homoskedasticity 00- 304 12 324 Hybrid Models 0 304 12 3 3 The TSLS_Regimes Command 307 12 331 Preliminaries 0 0 00 0 eee 307 12 332 Error by Regime 00 310 12 333 Homoskedasticity 2002 000006 310 12 334 Hybrid Models 0 eee 310 13 Spatial Regimes, Spatial Models 315 13 1 Basic Principles 6 ee ees 315 13 1 1 Model Specification © ee ee ee es 315 13 2 Spatial Regime Models in the GUT v2 317 13 2 1 Model Speeification «rennen 317 13 2 2 Spatial Lag Model with Regimes ----- 320 13 221 S2SLS Estimation 0 205 ee eee 320 13 2211 Fixed Spatial Lag Coefficient, Heteroskedas- ticity ee 320 13 2212 Fixed Spatial Lag Coefficient, Homoskedas- ticity ee ee 323 13 2213 Varying Spatial Lag Coefficient 323 13 222 ML Estimation 2 0 ee ee 325 13 2 3 Spatial Error model with Regimes 00 329 13 231 GM/GMM Estimation 46 329 13 232 ML Estimation 2202 eee ees 332 13 2 4 Combo Model with Regimes ---055 335 13 3 Spatial Regime Models in PySALspreg - cc 339 13 3 1 Model Specification © 6 ee ee ee 339 13 3 2 The GM_Lag Regimes Command --- 339 13 321 Fixed Lag Coefficient, Heteroskedasticity 340 13 322 Fixed Lag Coefficient, Homoskedasticity 340 13 323 Varying Lag Coefficients re 341 13 324 Hybrid Models 0 eee eee 341 13 3 3 The ML Lag_Regimes Command rer 344 13 331 Fixed Spatial Autoregressive Coefficient 344 13 332 Varying Spatial Autoregressive Coefficient 344 13 333 Hybrid Models ee eee ee 345 13 3 4 The GM_Error_Regimes Commands --+++--- 346 13 341 Fixed Spatial Error Coefficient -- 347 13 342 Varying Spatial Error Coefficient - 347 13 343 Hybrid Models - eee ere ees 347 13 3 5 The ML_Error_ Regimes Command rc 348 13 351 Fixed Spatial Error Coefficient - -+-- 349 13 352 Varying Spatial Error Coefficient --- 349 LIST OF FIGURES 13 32 Lag regimes, single constant (S2SLS), diagnostics - 342 13 33 Complex hybrid lag model (S2SLS) rc reen 343 13 34 Complex hybrid lag model (ML) ren cen 345 13 35 Complex hybrid error model (SW2SLS) --- +--+ --+- 348 13 36 Complex hybrid error model (ML) rc rer 13 37 Complex hybrid combo model (SW2SLS) rc 352
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spellingShingle	Anselin, Luc Rey, Sergio J. Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL Econometric models Space in economics Spatial analysis (Statistics) Ökonometrisches Modell Räumliche Statistik (DE-588)4386767-4 gnd
subject_GND	(DE-588)4386767-4
title	Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL
title_auth	Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL
title_exact_search	Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL
title_full	Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL Luc Anselin, Sergio J. Rey
title_fullStr	Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL Luc Anselin, Sergio J. Rey
title_full_unstemmed	Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL Luc Anselin, Sergio J. Rey
title_short	Modern spatial econometrics in practice
title_sort	modern spatial econometrics in practice a guide to geoda geodaspace and pysal
title_sub	a guide to GeoDa, GeoDaSpace and PySAL
topic	Econometric models Space in economics Spatial analysis (Statistics) Ökonometrisches Modell Räumliche Statistik (DE-588)4386767-4 gnd
topic_facet	Econometric models Space in economics Spatial analysis (Statistics) Ökonometrisches Modell Räumliche Statistik
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028152249&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT anselinluc modernspatialeconometricsinpracticeaguidetogeodageodaspaceandpysal AT reysergioj modernspatialeconometricsinpracticeaguidetogeodageodaspaceandpysal

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