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Introduction to Spatial Econometrics

ISBN-10: 142006424X

ISBN-13: 9781420064247

Edition: 2009

Authors: James P. LeSage, Robert Kelley Pace, R. Kelley Pace

List price: $104.95
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Understand the Complexities of Spatial Econometrics Spatial dependence is a frequent occurrence in sample data collected with reference to points or regions in space, such as with census data based on regions like census tracts, counties, or postal code areas. When spatial dependence is encountered in sample data, it leads to biased and inconsistent estimates arising from conventional regression-based econometric models. This book introduces readers to the econometric issues encountered when this occurs. It presents maximum likelihood and Bayesian spatial regression methods at a level appropriate for practitioners familiar with undergraduate-level introductory economic models and methods.
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Book details

List price: $104.95
Copyright year: 2009
Publisher: CRC Press LLC
Publication date: 1/20/2009
Binding: Hardcover
Pages: 374
Size: 6.25" wide x 9.25" long x 1.00" tall
Weight: 1.430
Language: English

Spatial dependence
The spatial autoregressive process
Spatial autoregressive data generating process
An illustration of spatial spillovers
The role of spatial econometric models
The plan of the text
Motivating and Interpreting Spatial Econometric Models
A time-dependence motivation
An omitted variables motivation
A spatial heterogeneity motivation
An externalities-based motivation
A model uncertainty motivation
Spatial autoregressive regression models
Interpreting parameter estimates
Direct and indirect impacts in theory
Calculating summary measures of impacts
Measures of dispersion for the impact estimates
Partitioning the impacts by order of neighbors
Simplified alternatives to the impact calculations
Chapter summary
Maximum Likelihood Estimation
Model estimation
SAR and SDM model estimation
SEM model estimation
Estimates for models with two weight matrices
Estimates of dispersion for the parameters
A mixed analytical-numerical Hessian calculation
A comparison of Hessian calculations
Omitted variables with spatial dependence
A Hausman test for OLS and SEM estimates
Omitted variables bias of least-squares
Omitted variables bias for spatial regressions
An applied example
Coefficient estimates
Cumulative effects estimates
Spatial partitioning of the impact estimates
A comparison of impacts from different models
Chapter summary
Log-determinants and Spatial Weights
Determinants and transformations
Basic determinant computation
Determinants of spatial systems
Scalings and similarity transformations
Determinant domain
Special cases
Monte Carlo approximation of the log-determinant
Sensitivity of � estimates to approximation
Chebyshev approximation
Determinant bounds
Inverses and other functions
Expressions for interpretation of spatial models
Closed-form solutions for single parameter spatial models
Forming spatial weights
Chapter summary
Bayesian Spatial Econometric Models
Bayesian methodology
Conventional Bayesian treatment of the SAR model
Analytical approaches to the Bayesian method
Analytical solution of the Bayesian spatial model
MCMC estimation of Bayesian spatial models
Sampling conditional distributions
Sampling for the parameter �
The MCMC algorithm
An applied illustration
Uses for Bayesian spatial models
Robust heteroscedastic spatial regression
Spatial effects estimates
Models with multiple weight matrices
Chapter summary
Model Comparison
Comparison of spatial and non-spatial models
An applied example of model comparison
The data sample used
Comparing models with different weight matrices
A test for dependence in technical knowledge
A test of the common factor restriction
Spatial effects estimates
Bayesian model comparison
Comparing models based on different weights
Comparing models based on different variables
An applied illustration of model comparison
An illustration of MC<sup>3</sup> and model averaging
Chapter summary
Chapter appendix
Spatiotemporal and Spatial Models
Spatiotemporal partial adjustment model
Relation between spatiotemporal and SAR models
Relation between spatiotemporal and SEM models
Covariance matrices
Monte Carlo experiment
Spatial econometric and statistical models
Patterns of temporal and spatial dependence
Chapter summary
Spatial Econometric Interaction Models
Interregional flows in a spatial regression context
Maximum likelihood and Bayesian estimation
Application of the spatial econometric interaction model
Extending the spatial econometric interaction model
Adjusting spatial weights using prior knowledge
Adjustments to address the zero flow problem
Spatially structured multilateral resistance effects
Flows as a rare event
Chapter summary
Matrix Exponential Spatial Models
The MESS model
The matrix exponential
Maximum likelihood estimation
A closed form solution for the parameters
An applied illustration
Spatial error models using MESS
Spatial model Monte Carlo experiments
An applied illustration
A Bayesian version of the model
The posterior for �
The posterior for �
Applied illustrations
Extensions of the model
More flexible weights
MCMC estimation
MCMC estimation of the model
The conditional distributions for �, � and V
Computational considerations
An illustration of the extended model
Fractional differencing
Empirical illustrations
Computational considerations
Chapter summary
Limited Dependent Variable Spatial Models
Bayesian latent variable treatment
The SAR probit model
An MCMC sampler for the SAR probit model
Gibbs sampling the conditional distribution for y*
Some observations regarding implementation
Applied illustrations of the spatial probit model
Marginal effects for the spatial probit model
The ordered spatial probit model
Spatial Tobit models
An example of the spatial Tobit model
The multinomial spatial probit model
The MCMC sampler for the SAR MNP model
Sampling for � and �
Sampling for �
Sampling for &ytilde;*
An applied illustration of spatial MNP
Effects estimates for the spatial MNP model
Spatially structured effects probit models
Chapter summary