Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples
type: Item
instance of: scholarly article
title: Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples
zbMATH Open document ID: 1400.62332
author: Pavlo Mozharovskyi
author: Jan Vogler
published in: Economics Letters
publication date: 2018-09-11T00:00:00Z
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 6934644
zbMATH Keywords: spatial probit models
zbMATH Keywords: sparse matrices
zbMATH Keywords: composite marginal likelihood
zbMATH Keywords: partial maximum likelihood
zbMATH Keywords: spatial econometrics
describes a project that uses: Arc_Mat
MaRDI profile type: Publication
full work available at URL: j.econlet.2016.09.022
OpenAlex ID: W3123653749
cites work: The composite marginal likelihood (CML) inference approach with applications to discrete and mixed dependent variable models
cites work: A Composite Likelihood Approach to Binary Spatial Data
cites work: Q3605139
cites work: Approximate Bayesian inference for hierarchical Gaussian Markov random field models
cites work: Partial maximum likelihood estimation of spatial probit models
DOI: 10.1016/J.ECONLET.2016.09.022
description: scientific article; zbMATH DE number 6934644