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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:
This book focuses on key research from new developments in econometrics.
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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 / R. Kelley Pace and James P. LeSage --
Likelihood evaluation of high-dimensional spatial latent Gaussian models with non-Gaussian response variables / Roman Liesenfeld, Jean-Franfois Richard and Jan Vogler --
The impact of storms on firm survival: a Bayesian spatial econometric model for firm survival / Mihaela Craioveanu and Dek Terrell --
Bayesian spatial bivariate panel probit estimation / Badi H. Baltagi, Peter H. Egger and Michaela Kesina --
Estimating binary spatial autoregressive models for rare events / Raffaella Calabrese and Johan A. Elkink --
A multivariate spatial analysis for anticipating new firm counts / Yiyi Wang, Kara M. Kockelman and Paul Damien --
A multivariate spatial-time of day analysis of truck crash frequency across neighborhoods in New York City / Wei Zou, Xiaokun Wang and Yiyi Wang --
Group interaction in research and the use of general nesting spatial models / Peter Burridge, J. Paul Elhorst and Katarina Zigova --
How to measure spillover effects of public capital stock a spatial autoregressive stochastic frontier model / Jaepil Han, Deockhyun Ryu and Robin Sickles --
Local marginal analysis of spatial data: a Gaussian process regression approach with Bayesian model and kernel averaging / Jacob Dearmon and Tony E. Smith --
City and industry network impacts on innovation by Chinese manufacturing firms: a hierarchical spatial-interindustry model / Yuxue Sheng and James P. LeSage.
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.

Abstract:

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

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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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