Spatial econometrics : qualitative and limited dependent variables

Spatial econometrics : qualitative and limited dependent variables / 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).

Advances in Econometrics is a research annual whose editorial policy is to publish original research articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research. Volume 37 exemplifies this focus...

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Bibliographic Details
Other Authors: Baltagi, Badi H. (Badi Hani) (Editor), LeSage, James P. (Editor), Pace, R. Kelley (Editor)
Format: eBook
Language:English
Published: Bingley, UK : Emerald Group Publishing Limited, 2017.
Edition:First edition.
Series:Advances in econometrics ; volume 37.
Subjects:
Econometrics.
Spatial analysis (Statistics)
spatial analysis.
BUSINESS & ECONOMICS > Economics > General.
BUSINESS & ECONOMICS > Reference.
Econometrics
Räumliche Statistik
Raumwirtschaftstheorie
Ökonometrisches Modell
Online Access:Click for online access
Table of 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.

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