A Monte Carlo EM method for estimating multinomial probit models.

DOI10.1016/S0167-9473(99)00073-0zbMath1052.62508MaRDI QIDQ1589453

Nicholas M. Kiefer, Charles E. McCulloch, Ranjini Natarajan

Publication date: 12 December 2000

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

Maximum likelihood estimationPanel dataGibbs samplingMonte Carlo EMMenu pricingObserved information

Mathematics Subject Classification ID

Related Items (9)

Sequential Monte Carlo EM for multivariate probit models ⋮ Maximum Likelihood Estimation of Tobit Factor Analysis for Multivariatet-Distribution ⋮ The Monte Carlo EM method for estimating multinomial probit latent variable models ⋮ Unnamed Item ⋮ Estimation in the probit normal model for binary outcomes using the SAEM algorithm ⋮ Maximum likelihood estimation of multinomial probit factor analysis models for multivariate \(t\)-distribution ⋮ Discrete choice models for ordinal response variables: a generalization of the stereotype model ⋮ Flexible random intercept models for binary outcomes using mixtures of normals ⋮ The Monte Carlo EM method for estimating multivariate tobit latent variable models

Uses Software

• GAUSS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models
• Statistical inference in the multinomial multiperiod probit model
• Unbalanced Repeated-Measures Models with Structured Covariance Matrices
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• A Method for Smoothing Simulated Moments of Discrete Probabilities in Multinomial Probit Models
• A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• A Computationally Practical Simulation Estimator for Panel Data
• Maximum Likelihood Variance Components Estimation for Binary Data
• A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration
• Monte Carlo EM Estimation for Time Series Models Involving Counts
• Bayesian Analysis of Binary and Polychotomous Response Data
• Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions.

This page was built for publication: A Monte Carlo EM method for estimating multinomial probit models.