Composite marginal likelihood estimation of spatial autoregressive probit models feasible in very large samples

DOI10.1016/j.econlet.2016.09.022zbMath1400.62332OpenAlexW3123653749MaRDI QIDQ1672731

Pavlo Mozharovskyi, Jan Vogler

Publication date: 11 September 2018

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.econlet.2016.09.022

zbMATH Keywords

sparse matricesspatial econometricscomposite marginal likelihoodpartial maximum likelihoodspatial probit models

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Partial ML estimation for spatial autoregressive nonlinear probit models with autoregressive disturbances

Uses Software

• Arc_Mat

Cites Work

• Unnamed Item
• Approximate Bayesian inference for hierarchical Gaussian Markov random field models
• Partial maximum likelihood estimation of spatial probit models
• The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models
• A Composite Likelihood Approach to Binary Spatial Data