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Spatial econometrics : qualitative and limited dependent variables

Author: Badi H Baltagi; James P LeSage; R Kelley Pace
Publisher: Bingley, UK : Emerald, 2017.
Series: Advances in econometrics, v. 37.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

Advances in Econometrics 37 highlights key research in econometrics in a user friendly way for economists who are not econometricians.

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Document Type: Book
All Authors / Contributors: Badi H Baltagi; James P LeSage; R Kelley Pace
ISBN: 1785609866 9781785609862
OCLC Number: 948826237
Description: xvi, 386 pages : illustrations, maps ; 24 cm.
Contents: Progress in spatial modeling of discrete and continuous dependent variables --
Fast simulated maximum likelihood estimation of the spatial probit model capable of handling large samples --
Likelihood evaluation of high-dimensional spatial latent Gaussian models with non-Gaussian response variables --
The impact of storms on firm survival: a Bayesian spatial econometric model for firm survival --
Bayesian spatial bivariate panel probit estimation --
Estimating binary spatial autoregressive models for rare events --
A multivariate spatial analysis for anticipating new firm counts --
A multivariate spatial-time of day analysis of truck crash frequency across neighborhoods in New York City --
Group interaction in research and the use of general nesting spatial models --
How to measure spillover effects of public capital stock a spatial autoregressive stochastic frontier model --
Local marginal analysis of spatial data: a Gaussian process regression approach with Bayesian model and kernel averaging --
City and industry network impacts on innovation by Chinese manufacturing firms: a hierarchical spatial-interindustry model.
Series Title: Advances in econometrics, v. 37.
Responsibility: edited by Badi H. Baltagi, Syracuse University, Syracuse, NY, USA ; James P. LeSage, Texas State University, San Marcos, TX, USA ; R. Kelley Pace, Louisiana State University, Baton Rouge, LA, USA.

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Seven of the eleven papers in this collection explain how to estimate discrete dependent variables with spatial dependence using maximum likelihood and how to estimate binary and count dependent Read more...

 
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