Bayesian analysis of nested logit model by Markov chain Monte Carlo.

DOI10.1016/S0304-4076(02)00125-2zbMath1079.62558OpenAlexW3125597289MaRDI QIDQ1868968

Jian Gao, Kajal Lahiri

Publication date: 9 April 2003

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0304-4076(02)00125-2

zbMATH Keywords

MCMCDiscrete choiceMixing speedRandom utility maximization

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

Tests for the consistency of three-level nested logit models with utility maximization ⋮ A Bayesian analysis of tree structure specification in nested logit models ⋮ Modelling the Evolution of Distributions: An Application to Major League Baseball

Uses Software

• LIMDEP
• Gibbsit

Cites Work

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• On the compatibility of nested logit models with utility maximization
• Testing the consistency of nested logit models with utility maximization
• Markov chains and stochastic stability
• A Bayesian analysis of nested logit models
• Bayesian analysis of logit models using natural conjugate priors
• Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms
• On the compatibility of nested logit models with utility maximization. A comment
• Inference from iterative simulation using multiple sequences
• A slowly mixing Markov chain with implications for Gibbs sampling
• Markov chains for exploring posterior distributions. (With discussion)
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• General Irreducible Markov Chains and Non-Negative Operators
• Applications of a Method for the Efficient Computation of Posterior Distributions
• Accurate Approximations for Posterior Moments and Marginal Densities
• Using simulation methods for bayesian econometric models: inference, development,and communication
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Efficient parametrisations for normal linear mixed models
• Annealing Markov Chain Monte Carlo with Applications to Ancestral Inference
• Gibbs Sampler Convergence Criteria
• Monte Carlo sampling methods using Markov chains and their applications