Introduction to Spatial Econometrics

James LeSage, Robert Kelley Pace

Hardback
$83.96

eBook
from $47.00

January 20, 2009 by Chapman and Hall/CRC
Professional - 374 Pages - 4 Color & 21 B/W Illustrations
ISBN 9781420064247 - CAT# C6424
Series: Statistics: A Series of Textbooks and Monographs

FREE Standard Shipping!

was $104.95

$83.96

SAVE $20.99

Add to Cart
Add to Wish List

Features

  • Presents formal, systematic motivations for spatial econometric regression models
  • Includes advanced models not found in other books, such as the matrix exponential and fractional differencing
  • Discusses simultaneous estimation of model parameters and model selection using MCMC model comparison methods and Bayesian model averaging
  • Offers unique, computational insights on determinants, weight matrices, and approaches for handling large spatial data sets
  • Addresses the economic implications of spatial models
  • Extends techniques to other settings, such as those with discrete dependent variables, flow data, spatiotemporal data, and panel data
  • Provides MATLAB code for implementing the methods on the authors’ websites

Summary

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 It encompasses many recent advances in spatial econometric models—including some previously unpublished results.