Modern spatial econometrics in practice: a guide to GeoDa, GeoDaSpace and PySAL
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
2014
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xx, 368 pages illustrations (some color) 23 cm |
| ISBN: | 9780986342103 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV042721058 | ||
| 003 | DE-604 | ||
| 005 | 20150917 | ||
| 007 | t | ||
| 008 | 150728s2014 a||| |||| 00||| eng d | ||
| 020 | |a 9780986342103 |9 978-0-9863-4210-3 | ||
| 035 | |a (OCoLC)923797910 | ||
| 035 | |a (DE-599)BVBBV042721058 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91 | ||
| 084 | |a GEO 007f |2 stub | ||
| 084 | |a WIR 017f |2 stub | ||
| 100 | 1 | |a Anselin, Luc |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Modern spatial econometrics in practice |b a guide to GeoDa, GeoDaSpace and PySAL |c Luc Anselin, Sergio J. Rey |
| 264 | 1 | |c 2014 | |
| 300 | |a xx, 368 pages |b illustrations (some color) |c 23 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Econometric models | |
| 650 | 4 | |a Space in economics | |
| 650 | 4 | |a Spatial analysis (Statistics) | |
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 0 | 7 | |a Räumliche Statistik |0 (DE-588)4386767-4 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Räumliche Statistik |0 (DE-588)4386767-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Rey, Sergio J. |e Verfasser |0 (DE-588)170952177 |4 aut | |
| 856 | 4 | 2 | |m HEBIS Datenaustausch |q application/pdf |u 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 |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-028152249 | ||
Datensatz im Suchindex
| _version_ | 1804174926831157248 |
|---|---|
| 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
|
| any_adam_object | 1 |
| author | Anselin, Luc Rey, Sergio J. |
| author_GND | (DE-588)170952177 |
| author_facet | Anselin, Luc Rey, Sergio J. |
| author_role | aut aut |
| author_sort | Anselin, Luc |
| author_variant | l a la s j r sj sjr |
| building | Verbundindex |
| bvnumber | BV042721058 |
| classification_tum | GEO 007f WIR 017f |
| ctrlnum | (OCoLC)923797910 (DE-599)BVBBV042721058 |
| discipline | Geowissenschaften Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV042721058 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:08:09Z |
| institution | BVB |
| isbn | 9780986342103 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028152249 |
| oclc_num | 923797910 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | xx, 368 pages illustrations (some color) 23 cm |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| record_format | marc |
| spelling | Anselin, Luc Verfasser aut Modern spatial econometrics in practice a guide to GeoDa, GeoDaSpace and PySAL Luc Anselin, Sergio J. Rey 2014 xx, 368 pages illustrations (some color) 23 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Econometric models Space in economics Spatial analysis (Statistics) Ökonometrisches Modell Räumliche Statistik (DE-588)4386767-4 gnd rswk-swf Räumliche Statistik (DE-588)4386767-4 s DE-604 Rey, Sergio J. Verfasser (DE-588)170952177 aut HEBIS Datenaustausch application/pdf 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 Inhaltsverzeichnis |
| 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 |
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