(Not in Introduction to Spatial Econometrics
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$89.95$80.96 - Hardback: 374 pages
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- Published: January 2009
- ISBN: 978-1-4200642-4-7
- Publisher: Chapman & Hall
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- By James LeSage, and Robert Kelley Pace.
- Series Edited by N. Balakrishnan, William R. Schucany and Edward G. Schilling.
Part of the Statistics: A Series of Textbooks and Monographs series
Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observations. It explores a wide range of alternative topics, including maximum likelihood and Bayesian estimation, various types of spatial regression specifications, and applied modeling situations involving different circumstances.
Leaders in this field, the authors clarify the often-mystifying phenomenon of simultaneous spatial dependence. By presenting new methods, they help with the interpretation of spatial regression models, especially ones that include spatial lags of the dependent variable. The authors also examine the relationship between spatiotemporal processes and long-run equilibrium states that are characterized by simultaneous spatial dependence. MATLAB® toolboxes useful for spatial econometric estimation are available on the authors’ websites.
This work covers spatial econometric modeling as well as numerous applied illustrations of the methods. It encompasses many recent advances in spatial econometric models—including some previously unpublished results.
Table of Contents
Introduction
Spatial dependence
The spatial autoregressive 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
Maximum Likelihood Estimation
Model estimation
Estimates of dispersion for the parameters
Omitted variables with spatial dependence
An applied example
Log-Determinants and Spatial Weights
Determinants and transformations
Basic determinant computation
Determinants of spatial systems
Monte Carlo approximation of the log-determinant
Chebyshev approximation
Extrapolation
Determinant bounds
Inverses and other functions
Expressions for interpretation of spatial models
Closed-form solutions for single parameter spatial models
Forming spatial weights
Bayesian Spatial Econometric Models
Bayesian methodology
Conventional Bayesian treatment of the SAR model
MCMC estimation of Bayesian spatial models
The MCMC algorithm
An applied illustration
Uses for Bayesian spatial models
Model Comparison
Comparison of spatial and non-spatial models
An applied example of model comparison
Bayesian model comparison
Chapter appendix
Spatiotemporal and Spatial Models
Spatiotemporal partial adjustment model
Relation between spatiotemporal and SAR models
Relation between spatiotemporal and SEM models
Covariance matrices
Spatial econometric and statistical models
Patterns of temporal and spatial dependence
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
Matrix Exponential Spatial Models
The MESS model
Spatial error models using MESS
A Bayesian version of the model
Extensions of the model
Fractional differencing
Limited Dependent Variable Spatial Models
Bayesian latent variable treatment
The ordered spatial probit model
Spatial Tobit models
The multinomial spatial probit model
An applied illustration of spatial MNP
Spatially structured effects probit models
References
A summary appears at the end of each chapter.
