Introduction to Spatial Econometrics

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A01=James P. LeSage
A01=Robert Kelley Pace
advanced econometric techniques
Author_James P. LeSage
Author_Robert Kelley Pace
Average Direct Impact
Average Total Impact
Bayesian inference
Category=KCH
Category=PBT
Conditional Posterior
Conditional Posterior Distribution
dependence
econometrics
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
explanatory
Higher Order Neighbors
lag
log-determinants and spatial weights
matrix
maximum likelihood estimation
maximum likelihood methods
MCMC Draw
MCMC Estimation
MCMC Sampler
model
models
Multivariate Truncated Normal Distribution
Omitted Variables Bias
Posterior Model Probabilities
regional innovation analysis
Regional Knowledge Stocks
regression
Sac Model
sar
Scalar Summary Measures
SDM Model
SEM Model
Spatial Autoregressive Process
spatial data
spatial data regression for researchers
Spatial Dependence
Spatial Dependence Parameter
Spatial Econometric Models
Spatial Lag
spatial panel data
Spatial Regression Models
Spatial Weight Matrices
Spatial Weight Matrix
spatiotemporal modeling
True DGP
variables
weight

Product details

  • ISBN 9781420064247
  • Weight: 860g
  • Dimensions: 156 x 234mm
  • Publication Date: 20 Jan 2009
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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.

James LeSage, Robert Kelley Pace

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