Discrete Choice Methods with Simulation by Kenneth E. Train

  title={Discrete Choice Methods with Simulation by Kenneth E. Train},
  author={Kenneth E. Train},
  • K. Train
  • Published 2003
  • Computer Science, Economics

Discrete Choice Methods with Simulation

Discrete Choice Methods with Simulation by Kenneth Train has been available in the second edition since 2009 and contains two additional chapters, one on endogenous regressors and one on the expectation–maximization (EM) algorithm.

On estimation of Hybrid Choice Models

The classical estimation technique for a simulated maximum likelihood (SML) solution of the HCM is described and it is shown that although HCM joint estimation requires the evaluation of complex multi-dimensional integrals, SML can be successfully implemented.

Efficiently Estimating Nested Logit Models with Choice-Based Samples

Choice-based samples oversample infrequently chosen alternatives to obtain an effective representation of the behavior of people who select these alternatives. However, the use of choice-based

Maximum Simulated Likelihood Methods and Applications

This volume is a collection of methodological developments and applications of simulation-based methods that were presented at a workshop at Louisiana State University in November, 2009. The first

Discrete Choice Analysis

This chapter gives an overview of discrete choice analysis techniques. First we present a reflection about the meaning of the words ‘discrete’ and ‘choice’. Then we provide an overview of the sorts

Discrete Choice Models with Alternate Kernel Error Distributions

  • Rajesh Paleti
  • Business
    Journal of the Indian Institute of Science
  • 2019
This paper compiles a synthesis of the past literature that developed choices models with flexible kernel errors, including both parametric and semi-parametric methods and concludes with possible avenues for further research.

Maximum Simulated Likelihood Estimation: Techniques and Applications in Economics

This chapter discusses maximum simulated likelihood estimation when construction of the likelihood function is carried out by recently proposed Markov chain Monte Carlo methods and implements the methodology in a study of the joint behavior of four categories of U.S. technology patents using a copula model for multivariate count data.

On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects

An extensive simulation experiment is conducted and a model selection heuristic is developed that identifies the correct market in 81% of the experimental conditions, while strict application of the best model selection criterion alone results in correct market identification in at most 34% of experimental conditions.

Discrete Choice Models for Utility and Probability in Empirical Bayes estimation

This paper describes an easy and convenient empirical Bayesian way to construct priors and combine them with the likelihood on individual level data that allows the modeler to obtain posterior estimation of MNL utilities in noniterative evaluations.

Estimation of Multinomial Logit Models with Unobserved Heterogeneity using Maximum Simulated Likelihood

In this paper, we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is



A Comparison of Hierarchical Bayes and Maximum Simulated Likelihood for Mixed Logit

Mixed logit is a flexible discrete choice model that allows for random coefficients and/or error components that induce correlation over alternatives and time. Procedures for estimating mixed logits

Markov Chain Monte Carlo Simulation Methods in Econometrics

This paper summarizes some of the relevant theoretical literature and presents several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics, including the Gibbs sampler.

An Empirical Investigation of the Consistency of Nested Logit Models with Utility Maximization

Global conditions under which nested logit models are consistent with utility maximization are provided by Daly and Zachary and by McFadden. Recently, Borsch-Supan and Herriges and Kling have


This paper considers mixed, or random coefficients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice

Alternative computational approaches to inference in the multinomial probit model

This research compares several approaches to inference in the multinomial probit model, based on Monte-Carlo results for a seven choice model, and finds that the Gibbs sampling-data augmentation algorithm appears to have a slight overall edge.

Taste variation in discrete choice models

This paper develops an extension of the classical multinomial logit model which approximates a class of models obtained when there is uncontrolled taste variation across agents and choices in

Markov Chain Monte Carlo in Practice: A Roundtable Discussion

Advice and guidance is offered to novice users of MCMC to help them build confidence in simulation results, methods for speeding and assessing convergence, estimating standard error, and more.

An exact likelihood analysis of the multinomial probit model