Introduction to spatial econometrics:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla.
CRC Press
2009
|
| Series: | Statistics
196 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIII, 354 S. graph. Darst., Kt. |
| ISBN: | 9781420064247 142006424X |
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| 100 | 1 | |a LeSage, James |e Verfasser |0 (DE-588)1089475780 |4 aut | |
| 245 | 1 | 0 | |a Introduction to spatial econometrics |c James LeSage, R. Kelley Pace |
| 264 | 1 | |a Boca Raton, Fla. |b CRC Press |c 2009 | |
| 300 | |a XIII, 354 S. |b graph. Darst., Kt. | ||
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| 490 | 1 | |a Statistics |v 196 | |
| 500 | |a Includes bibliographical references and index | ||
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| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 4 | |a Space in economics |x Econometric models | |
| 650 | 4 | |a Space in economics |x Mathematical models | |
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Record in the Search Index
| _version_ | 1804138364186656768 |
|---|---|
| adam_text | Contents
1
Introduction
1
1.1
Spatial
dependence
. ...................... 1
1.2
The spatial
autoregressive
process
............... 8
1.2.1
Spatial
autoregressive data
generating process
..... 12
1.3
An illustration of spatial spillovers
............... 16
1.4
The role of spatial econometric models
............ 20
1.5
The plan of the text
....................... 22
2
Motivating and Interpreting Spatial Econometric Models
25
2.1
A time-dependence motivation
................. 25
2.2
An omitted variables motivation
................ 27
2.3
A spatial heterogeneity motivation
............... 29
2.4
An externalities-based motivation
............... 30
2.5
A model uncertainty motivation
................ 30
2.6
Spatial
autoregressive
regression models
............ 32
2.7
Interpreting parameter estimates
................ 33
2.7.1
Direct and indirect impacts in theory
......... 34
2.7.2
Calculating summary measures of impacts
....... 39
2.7.3
Measures of dispersion for the impact estimates
.... 39
2.7.4
Partitioning the impacts by order of neighbors
.... 40
2.7.5
Simplified alternatives to the impact calculations
... 41
2.8
Chapter summary
........................ 42
3
Maximum Likelihood Estimation
45
3.1
Model estimation
........................ 46
3.1.1 SAR
and SDM model estimation
............ 46
3.1.2 SEM
model estimation
.................. 50
3.1.3
Estimates for models with two weight matrices
.... 52
3.2
Estimates of dispersion for the parameters
.......... 54
3.2.1
A mixed analytical-numerical Hessian calculation
... 56
3.2.2
A comparison of Hessian calculations
.......... 59
3.3
Omitted variables with spatial dependence
.......... 60
3.3.1
A Hausman test for OLS and
SEM
estimates
..... 61
3.3.2
Omitted variables bias of least-squares
......... 63
3.3.3
Omitted variables bias for spatial regressions
..... 67
3.4
An applied example
....................... 68
3.4.1
Coefficient estimates
................... 69
3.4.2
Cumulative
effects estimates
.............. 70
3.4.3
Spatial partitioning of the impact estimates
...... 72
3.4.4
A comparison of impacts from different models
.... 73
3.5
Chapter summary
........................ 75
Log-determinants and Spatial Weights
77
4.1
Determinants and transformations
............... 77
4.2
Basic determinant computation
................ 81
4.3
Determinants of spatial systems
................ 84
4.3.1
Scalings and similarity transformations
........ 87
4.3.2
Determinant domain
................... 88
4.3.3
Special cases
........................ 89
4.4
Monte Carlo approximation of the log-determinant
...... 96
4.4.1
Sensitivity of
ρ
estimates to approximation
...... 100
4.5
Chebyshev approximation
.................... 105
4.6
Extrapolation
.......................... 108
4.7
Determinant bounds
....................... 108
4.8
Inverses and other functions
.................. 110
4.9
Expressions for interpretation of spatial models
........ 114
4.10
Closed-form solutions for single parameter spatial models
. . 116
4.11
Forming spatial weights
..................... 118
4.12
Chapter summary
........................ 120
Bayesian Spatial Econometric Models
123
5.1
Bayesian methodology
...................... 124
5.2
Conventional Bayesian treatment of the
SAR
model
..... 127
5.2.1
Analytical approaches to the Bayesian method
.... 127
5.2.2
Analytical solution of the Bayesian spatial model
. . . 130
5.3
MCMC estimation of Bayesian spatial models
........ 133
5.3.1
Sampling conditional distributions
........... 133
5.3.2
Sampling for the parameter
ρ
.............. 136
5.4
The MCMC algorithm
..................... 139
5.5
An applied illustration
..................... 142
5.6
Uses for Bayesian spatial models
................ 145
5.6.1
Robust heteroscedastic spatial regression
....... 146
5.6.2
Spatial effects estimates
................. 149
5.6.3
Models with multiple weight matrices
......... 150
5.7
Chapter summary
........................ 152
Model Comparison
155
6.1
Comparison of spatial and non-spatial models
........ 155
6.2
An applied example of model comparison
........... 159
6.2.1
The data sample used
.................. 161
6.2.2
Comparing models with different weight matrices
... 161
6.2.3
A test for dependence in technical knowledge
..... 163
Ill
6.2.4
A test of the common factor restriction
........ 164
6.2.5
Spatial effects estimates
................. 165
6.3
Bayesian model comparison
................... 168
6.3.1
Comparing models based on different weights
..... 169
6.3.2
Comparing models based on different variables
.... 173
6.3.3
An applied illustration of model comparison
...... 175
6.3.4
An illustration of
MC3
and model averaging
..... 178
6.4
Chapter summary
........................ 184
6.5
Chapter appendix
........................ 185
Spatiotemporal
and Spatial Models
189
7.1
Spatiotemporal
partial adjustment model
........... 190
7.2
Relation between
spatiotemporal
and
SAR
models
...... 191
7.3
Relation between
spatiotemporal
and
SEM
models
...... 196
7.4
Covariance matrices
....................... 197
7.4.1
Monte Carlo experiment
................. 200
7.5
Spatial econometric and statistical models
.......... 201
7.6
Patterns of temporal and spatial dependence
......... 203
7.7
Chapter summary
........................ 207
Spatial Econometric Interaction Models
211
8.1
Interregional flows in a spatial regression context
....... 212
8.2
Maximum likelihood and Bayesian estimation
........ 218
8.3
Application of the spatial econometric interaction model
. . 223
8.4
Extending the spatial econometric interaction model
..... 228
8.4.1
Adjusting spatial weights using prior knowledge
.... 229
8.4.2
Adjustments to address the zero flow problem
..... 230
8.4.3
Spatially structured multilateral resistance effects
. . . 232
8.4.4
Flows as a rare event
................... 234
8.5
Chapter summary
........................ 236
Matrix Exponential Spatial Models
237
9.1
The MESS model
........................ 237
9.1.1
The matrix exponential
................. 238
9.1.2
Maximum likelihood estimation
............. 239
9.1.3
A closed form solution for the parameters
....... 240
9.1.4
An applied illustration
.................. 241
9.2
Spatial error models using MESS
............... 243
9.2.1
Spatial model Monte Carlo experiments
........ 246
9.2.2
An applied illustration
.................. 247
9.3
A Bayesian version of the model
................ 250
9.3.1
The posterior for
α
.................... 250
9.3.2
The posterior for
β
....................
252
9.3.3
Applied illustrations
................... 253
9.4
Extensions of the model
..................... 255
IV
9.4.1
More flexible weights
................... 255
9.4.2
MCMC estimation
.................... 256
9.4.3
MCMC estimation of the model
............ 257
9.4.4
The conditional distributions for
β, σ
and V
...... 258
9.4.5
Computational considerations
.............. 259
9.4.6
An illustration of the extended model
......... 260
9.5
Fractional differencing
...................... 265
9.5.1
Empirical illustrations
.................. 270
9.5.2
Computational considerations
.............. 275
9.6
Chapter summary
........................ 277
10
Limited Dependent Variable Spatial Models
279
10.1
Bayesian latent variable treatment
............... 281
10.1.1
The
SAR
probit
model
.................. 283
10.1.2
An MCMC sampler for the
SAR
probit
model
.... 284
10.1.3
Gibbs sampling the conditional distribution for y*
. . 285
10.1.4
Some observations regarding implementation
..... 287
10.1.5
Applied illustrations of the spatial
probit
model
. . . . 289
10.1.6
Marginal effects for the spatial
probit
model
...... 293
10.2
The ordered spatial
probit
model
............... 297
10.3
Spatial Tobit models
...................... 299
10.3.1
An example of the spatial Tobit model
......... 302
10.4
The multinomial spatial
probit
model
............. 306
10.4.1
The MCMC sampler for the
SAR MNP
model
.... 307
10.4.2
Sampling for
β
and
ρ
................... 308
10.4.3
Sampling for
Σ
...................... 308
10.4.4
Sampling for
jí*
...................... 310
10.5
An applied illustration of spatial MNP
............ 312
10.5.1
Effects estimates for the spatial MNP model
..... 314
10.6
Spatially structured effects
probit
models
........... 316
10.7
Chapter summary
........................ 320
References
323
Index
337
|
| adam_txt |
Contents
1
Introduction
1
1.1
Spatial
dependence
. . 1
1.2
The spatial
autoregressive
process
. 8
1.2.1
Spatial
autoregressive data
generating process
. 12
1.3
An illustration of spatial spillovers
. 16
1.4
The role of spatial econometric models
. 20
1.5
The plan of the text
. 22
2
Motivating and Interpreting Spatial Econometric Models
25
2.1
A time-dependence motivation
. 25
2.2
An omitted variables motivation
. 27
2.3
A spatial heterogeneity motivation
. 29
2.4
An externalities-based motivation
. 30
2.5
A model uncertainty motivation
. 30
2.6
Spatial
autoregressive
regression models
. 32
2.7
Interpreting parameter estimates
. 33
2.7.1
Direct and indirect impacts in theory
. 34
2.7.2
Calculating summary measures of impacts
. 39
2.7.3
Measures of dispersion for the impact estimates
. 39
2.7.4
Partitioning the impacts by order of neighbors
. 40
2.7.5
Simplified alternatives to the impact calculations
. 41
2.8
Chapter summary
. 42
3
Maximum Likelihood Estimation
45
3.1
Model estimation
. 46
3.1.1 SAR
and SDM model estimation
. 46
3.1.2 SEM
model estimation
. 50
3.1.3
Estimates for models with two weight matrices
. 52
3.2
Estimates of dispersion for the parameters
. 54
3.2.1
A mixed analytical-numerical Hessian calculation
. 56
3.2.2
A comparison of Hessian calculations
. 59
3.3
Omitted variables with spatial dependence
. 60
3.3.1
A Hausman test for OLS and
SEM
estimates
. 61
3.3.2
Omitted variables bias of least-squares
. 63
3.3.3
Omitted variables bias for spatial regressions
. 67
3.4
An applied example
. 68
3.4.1
Coefficient estimates
. 69
3.4.2
Cumulative
effects estimates
. 70
3.4.3
Spatial partitioning of the impact estimates
. 72
3.4.4
A comparison of impacts from different models
. 73
3.5
Chapter summary
. 75
Log-determinants and Spatial Weights
77
4.1
Determinants and transformations
. 77
4.2
Basic determinant computation
. 81
4.3
Determinants of spatial systems
. 84
4.3.1
Scalings and similarity transformations
. 87
4.3.2
Determinant domain
. 88
4.3.3
Special cases
. 89
4.4
Monte Carlo approximation of the log-determinant
. 96
4.4.1
Sensitivity of
ρ
estimates to approximation
. 100
4.5
Chebyshev approximation
. 105
4.6
Extrapolation
. 108
4.7
Determinant bounds
. 108
4.8
Inverses and other functions
. 110
4.9
Expressions for interpretation of spatial models
. 114
4.10
Closed-form solutions for single parameter spatial models
. . 116
4.11
Forming spatial weights
. 118
4.12
Chapter summary
. 120
Bayesian Spatial Econometric Models
123
5.1
Bayesian methodology
. 124
5.2
Conventional Bayesian treatment of the
SAR
model
. 127
5.2.1
Analytical approaches to the Bayesian method
. 127
5.2.2
Analytical solution of the Bayesian spatial model
. . . 130
5.3
MCMC estimation of Bayesian spatial models
. 133
5.3.1
Sampling conditional distributions
. 133
5.3.2
Sampling for the parameter
ρ
. 136
5.4
The MCMC algorithm
. 139
5.5
An applied illustration
. 142
5.6
Uses for Bayesian spatial models
. 145
5.6.1
Robust heteroscedastic spatial regression
. 146
5.6.2
Spatial effects estimates
. 149
5.6.3
Models with multiple weight matrices
. 150
5.7
Chapter summary
. 152
Model Comparison
155
6.1
Comparison of spatial and non-spatial models
. 155
6.2
An applied example of model comparison
. 159
6.2.1
The data sample used
. 161
6.2.2
Comparing models with different weight matrices
. 161
6.2.3
A test for dependence in technical knowledge
. 163
Ill
6.2.4
A test of the common factor restriction
. 164
6.2.5
Spatial effects estimates
. 165
6.3
Bayesian model comparison
. 168
6.3.1
Comparing models based on different weights
. 169
6.3.2
Comparing models based on different variables
. 173
6.3.3
An applied illustration of model comparison
. 175
6.3.4
An illustration of
MC3
and model averaging
. 178
6.4
Chapter summary
. 184
6.5
Chapter appendix
. 185
Spatiotemporal
and Spatial Models
189
7.1
Spatiotemporal
partial adjustment model
. 190
7.2
Relation between
spatiotemporal
and
SAR
models
. 191
7.3
Relation between
spatiotemporal
and
SEM
models
. 196
7.4
Covariance matrices
. 197
7.4.1
Monte Carlo experiment
. 200
7.5
Spatial econometric and statistical models
. 201
7.6
Patterns of temporal and spatial dependence
. 203
7.7
Chapter summary
. 207
Spatial Econometric Interaction Models
211
8.1
Interregional flows in a spatial regression context
. 212
8.2
Maximum likelihood and Bayesian estimation
. 218
8.3
Application of the spatial econometric interaction model
. . 223
8.4
Extending the spatial econometric interaction model
. 228
8.4.1
Adjusting spatial weights using prior knowledge
. 229
8.4.2
Adjustments to address the zero flow problem
. 230
8.4.3
Spatially structured multilateral resistance effects
. . . 232
8.4.4
Flows as a rare event
. 234
8.5
Chapter summary
. 236
Matrix Exponential Spatial Models
237
9.1
The MESS model
. 237
9.1.1
The matrix exponential
. 238
9.1.2
Maximum likelihood estimation
. 239
9.1.3
A closed form solution for the parameters
. 240
9.1.4
An applied illustration
. 241
9.2
Spatial error models using MESS
. 243
9.2.1
Spatial model Monte Carlo experiments
. 246
9.2.2
An applied illustration
. 247
9.3
A Bayesian version of the model
. 250
9.3.1
The posterior for
α
. 250
9.3.2
The posterior for
β
.
252
9.3.3
Applied illustrations
. 253
9.4
Extensions of the model
. 255
IV
9.4.1
More flexible weights
. 255
9.4.2
MCMC estimation
. 256
9.4.3
MCMC estimation of the model
. 257
9.4.4
The conditional distributions for
β, σ
and V
. 258
9.4.5
Computational considerations
. 259
9.4.6
An illustration of the extended model
. 260
9.5
Fractional differencing
. 265
9.5.1
Empirical illustrations
. 270
9.5.2
Computational considerations
. 275
9.6
Chapter summary
. 277
10
Limited Dependent Variable Spatial Models
279
10.1
Bayesian latent variable treatment
. 281
10.1.1
The
SAR
probit
model
. 283
10.1.2
An MCMC sampler for the
SAR
probit
model
. 284
10.1.3
Gibbs sampling the conditional distribution for y*
. . 285
10.1.4
Some observations regarding implementation
. 287
10.1.5
Applied illustrations of the spatial
probit
model
. . . . 289
10.1.6
Marginal effects for the spatial
probit
model
. 293
10.2
The ordered spatial
probit
model
. 297
10.3
Spatial Tobit models
. 299
10.3.1
An example of the spatial Tobit model
. 302
10.4
The multinomial spatial
probit
model
. 306
10.4.1
The MCMC sampler for the
SAR MNP
model
. 307
10.4.2
Sampling for
β
and
ρ
. 308
10.4.3
Sampling for
Σ
. 308
10.4.4
Sampling for
jí*
. 310
10.5
An applied illustration of spatial MNP
. 312
10.5.1
Effects estimates for the spatial MNP model
. 314
10.6
Spatially structured effects
probit
models
. 316
10.7
Chapter summary
. 320
References
323
Index
337 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | LeSage, James Pace, R. Kelley |
| author_GND | (DE-588)1089475780 (DE-588)170539512 |
| author_facet | LeSage, James Pace, R. Kelley |
| author_role | aut aut |
| author_sort | LeSage, James |
| author_variant | j l jl r k p rk rkp |
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| discipline | Raumplanung Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Raumplanung Agrar-/Forst-/Ernährungs-/Haushaltswissenschaft / Gartenbau Mathematik Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4151278-9 Einführung gnd-content |
| genre_facet | Einführung |
| id | DE-604.BV035187667 |
| illustrated | Illustrated |
| index_date | 2024-07-02T23:00:26Z |
| indexdate | 2024-07-09T21:27:01Z |
| institution | BVB |
| isbn | 9781420064247 142006424X |
| language | English |
| lccn | 2008038890 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016994327 |
| oclc_num | 166358420 |
| open_access_boolean | |
| owner | DE-703 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-11 DE-521 DE-N2 DE-355 DE-BY-UBR DE-739 DE-M49 DE-BY-TUM DE-83 DE-384 DE-1050 |
| owner_facet | DE-703 DE-91G DE-BY-TUM DE-19 DE-BY-UBM DE-11 DE-521 DE-N2 DE-355 DE-BY-UBR DE-739 DE-M49 DE-BY-TUM DE-83 DE-384 DE-1050 |
| physical | XIII, 354 S. graph. Darst., Kt. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | CRC Press |
| record_format | marc |
| series | Statistics |
| series2 | Statistics |
| spelling | LeSage, James Verfasser (DE-588)1089475780 aut Introduction to spatial econometrics James LeSage, R. Kelley Pace Boca Raton, Fla. CRC Press 2009 XIII, 354 S. graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Statistics 196 Includes bibliographical references and index Regressieanalyse gtt Mathematisches Modell Ökonometrisches Modell Space in economics Econometric models Space in economics Mathematical models Raumwirtschaftstheorie (DE-588)4121557-6 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Raumwirtschaftstheorie (DE-588)4121557-6 s Ökonometrisches Modell (DE-588)4043212-9 s DE-604 Pace, R. Kelley Verfasser (DE-588)170539512 aut Statistics 196 (DE-604)BV000003265 196 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016994327&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | LeSage, James Pace, R. Kelley Introduction to spatial econometrics Statistics Regressieanalyse gtt Mathematisches Modell Ökonometrisches Modell Space in economics Econometric models Space in economics Mathematical models Raumwirtschaftstheorie (DE-588)4121557-6 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4121557-6 (DE-588)4043212-9 (DE-588)4151278-9 |
| title | Introduction to spatial econometrics |
| title_auth | Introduction to spatial econometrics |
| title_exact_search | Introduction to spatial econometrics |
| title_exact_search_txtP | Introduction to spatial econometrics |
| title_full | Introduction to spatial econometrics James LeSage, R. Kelley Pace |
| title_fullStr | Introduction to spatial econometrics James LeSage, R. Kelley Pace |
| title_full_unstemmed | Introduction to spatial econometrics James LeSage, R. Kelley Pace |
| title_short | Introduction to spatial econometrics |
| title_sort | introduction to spatial econometrics |
| topic | Regressieanalyse gtt Mathematisches Modell Ökonometrisches Modell Space in economics Econometric models Space in economics Mathematical models Raumwirtschaftstheorie (DE-588)4121557-6 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Regressieanalyse Mathematisches Modell Ökonometrisches Modell Space in economics Econometric models Space in economics Mathematical models Raumwirtschaftstheorie Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016994327&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000003265 |
| work_keys_str_mv | AT lesagejames introductiontospatialeconometrics AT pacerkelley introductiontospatialeconometrics |