Introduction to spatial econometrics /
Lesage, James P.
Introduction to spatial econometrics / James LeSage, R. Kelley Pace. - Boca Raton ; London ; New York : CRC Press : Taylor & Francis Group, 2009. - xiii, 354 páginas : tablas, gráficas ; 25 cm. - Statistics: textbooks and monographs ; 196 .
Incluye referencias bibliográficas (páginas 323-336) e índice.
1. Introduction: 1.1. Spatial dependence ; 1.2. The spatial autoregressive process ; 1.3. An illustration of spatial spillovers ; 1.4. The role of spatial econometric models ; 1.5. The plan of the text -- 2. Motivating and Interpreting Spatial Econometric Models: 2.1. A time-dependence motivation ; 2.2. An omitted variables motivation ; 2.3. A spatial heterogeneity motivation ; 2.4. An externalities-based motivation ; 2.5. A model uncertainty motivation ; 2.6. Spatial autoregressive regression models ; 2.7. Interpreting parameter estimates -- 3. Maximum Likelihood Estimation: 3.1. Model estimation ; 3.2. Estimates of dispersion for the parameters ; 3.3. Omitted variables with spatial dependence ; 3.4. An applied example -- 4. Log-determinants and spatial weighs: 4.1. Determinants and transformations ; 4.2. Basic determinant computation ; 4.3. Determinants of spatial systems ; 4.4. Monte Carlo approximation of the log-determinant ; 4.5. Chebyshev approximation ; 4.6. Extrapolation ; 4.7. Determinant bounds ; 4.8. Inverses and other functions ; 4.9. Expressions for interpretation of spatial models ; 4.10. Closed-form solutions for single parameter spatial models ; 4.11. Forming spatial weights -- 5. Bayesian Spatial Econometric Models: 5.1. Bayesian methodology ; 5.2. Conventional Bayesian treatment of the SAR model ; 5.3. MCMC estimation of Bayesian spatial models ; 5.4. The MCMC algorithm ; 5.5. An applied illustration ; 5.6. Uses for Bayesian spatial models – 6. Model Comparison: 6.1. Comparison of spatial and non-spatial models ; 6.2. An applied example of model comparison ; 6.3. Bayesian model comparison -- 7. Spatiotemporal and Spatial Models: 7.1. Spatiotemporal partial adjustment model ; 7.2. Relation between spatiotemporal and SAR models ; 7.3. Relation between spatiotemporal and SEM models ; 7.4. Covariance matrices – 7.5. Spatial econometric and statistical models ; 7.6. Patterns of temporal and spatial dependence -- 8. Spatial Econometric Interaction Models: 8.1. Interregional flows in a spatial regression context ; 8.2. Maximum likelihood and Bayesian estimation ; 8.3. Application of the spatial econometric interaction model ; 8.4. Extending the spatial econometric interaction model -- 9. Matrix exponential spatial models: 9.1. The MESS model ; 9.2. Spatial error models using MESS ; 9.3. A Bayesian version of the model ; 9.4 Extensions of the model ; 9.5. Fractional differencing -- 10. Limited dependent variable spatial Models: 10.1. Bayesian latent variable treatment ; 10.2. The ordered spatial probit model ; 10.3. Spatial Tobit models ; 10.4. The multinomial spatial probit model ; 10.5. An applied illustration of spatial MNP ; 10.6. Spatially structured effects probit models.
9781420064247 142006424X
330.015195 / W66i
Introduction to spatial econometrics / James LeSage, R. Kelley Pace. - Boca Raton ; London ; New York : CRC Press : Taylor & Francis Group, 2009. - xiii, 354 páginas : tablas, gráficas ; 25 cm. - Statistics: textbooks and monographs ; 196 .
Incluye referencias bibliográficas (páginas 323-336) e índice.
1. Introduction: 1.1. Spatial dependence ; 1.2. The spatial autoregressive process ; 1.3. An illustration of spatial spillovers ; 1.4. The role of spatial econometric models ; 1.5. The plan of the text -- 2. Motivating and Interpreting Spatial Econometric Models: 2.1. A time-dependence motivation ; 2.2. An omitted variables motivation ; 2.3. A spatial heterogeneity motivation ; 2.4. An externalities-based motivation ; 2.5. A model uncertainty motivation ; 2.6. Spatial autoregressive regression models ; 2.7. Interpreting parameter estimates -- 3. Maximum Likelihood Estimation: 3.1. Model estimation ; 3.2. Estimates of dispersion for the parameters ; 3.3. Omitted variables with spatial dependence ; 3.4. An applied example -- 4. Log-determinants and spatial weighs: 4.1. Determinants and transformations ; 4.2. Basic determinant computation ; 4.3. Determinants of spatial systems ; 4.4. Monte Carlo approximation of the log-determinant ; 4.5. Chebyshev approximation ; 4.6. Extrapolation ; 4.7. Determinant bounds ; 4.8. Inverses and other functions ; 4.9. Expressions for interpretation of spatial models ; 4.10. Closed-form solutions for single parameter spatial models ; 4.11. Forming spatial weights -- 5. Bayesian Spatial Econometric Models: 5.1. Bayesian methodology ; 5.2. Conventional Bayesian treatment of the SAR model ; 5.3. MCMC estimation of Bayesian spatial models ; 5.4. The MCMC algorithm ; 5.5. An applied illustration ; 5.6. Uses for Bayesian spatial models – 6. Model Comparison: 6.1. Comparison of spatial and non-spatial models ; 6.2. An applied example of model comparison ; 6.3. Bayesian model comparison -- 7. Spatiotemporal and Spatial Models: 7.1. Spatiotemporal partial adjustment model ; 7.2. Relation between spatiotemporal and SAR models ; 7.3. Relation between spatiotemporal and SEM models ; 7.4. Covariance matrices – 7.5. Spatial econometric and statistical models ; 7.6. Patterns of temporal and spatial dependence -- 8. Spatial Econometric Interaction Models: 8.1. Interregional flows in a spatial regression context ; 8.2. Maximum likelihood and Bayesian estimation ; 8.3. Application of the spatial econometric interaction model ; 8.4. Extending the spatial econometric interaction model -- 9. Matrix exponential spatial models: 9.1. The MESS model ; 9.2. Spatial error models using MESS ; 9.3. A Bayesian version of the model ; 9.4 Extensions of the model ; 9.5. Fractional differencing -- 10. Limited dependent variable spatial Models: 10.1. Bayesian latent variable treatment ; 10.2. The ordered spatial probit model ; 10.3. Spatial Tobit models ; 10.4. The multinomial spatial probit model ; 10.5. An applied illustration of spatial MNP ; 10.6. Spatially structured effects probit models.
9781420064247 142006424X
330.015195 / W66i