ASSEE 2008 - Discrete Choice Modelling

The topic of the 3rd Advanced Summer School was on “Discrete Choice Modelling“.

Professor William H. Greene (Department of Economics, Stern Business School, New York University) was the Distinguished Guest Professor. The lectures of the Summer School provided an up-to-date coverage of the main methods and models used in discrete choice modelling. The course examined familiar, basic methods and frontier developments in the field.

Course Description

Theory and Model Development

  1. Basic Building Blocks
      • Linear Regression
        • Regression model
        • Interpretation
        • Marginal effects
        • Robust estimation
      • Binary Choice
        • Underlying specification
        • Estimation
          • Maximum likelihood
          • Semiparametric estimation
        • Interpretation: Marginal effects
        • Specification analysis
        • The analysis of binary choices
        • Extensions: Panel data and heterogeneity
  2. Models for Discrete Choice Among Multiple Alternatives
      • Underlying theory: The random utility model
      • Multinomial logit models for multinomial choice
        • Estimation
        • Analysis: Marginal effects
        • Simulation
        • Fit and prediction
      • Extensions of the multinomial logit model
        • Nested logit models
        • Heteroscedasticity and heterogeneity
        • Mixed logit models and random parameters models
        • Kernel logit models
        • The multinomial probit model
      • Specification issues in discrete choice models
        • Stated and revealed preference data
        • Choice sets and attribute sets
  3. Model extensions
      • Multinomial and multivariate probit models
      • Panel data models
        • Fixed and random effects
        • Random parameters
        • Modeling heterogeity
  4. Models for counts
      • Poisson regression
      • Dispersion and heterogeneity: Negative binomial model
      • Models for panel data
      • Specification issues in count data models
  5. Special topics
      • Sample selection models
      • Censoring and truncation
      • Simulation based estimation
      • Models for panel data: Random parameter models
      • Fixed and random effects
      • Estimation: Bayesian and Maximum likelihood estimation

Computer Lab Sessions and Hands on Data

Practical examples and open discussions will take place in the second session in the morning and afternoon meetings. In these sessions we will:

  1. Learn how to fit and analyze discrete choice models
  2. Discuss philosophical, practical and technical issues.
  3. Discuss applications of the techniques that have appeared in the literature
  4. Estimate models using real data. Carry out exercises using NLOGIT.
  5. Develop applications to be discussed on the last day of class.

The software package used in the course will be NLOGIT, written by the instructor. Various related materials will be distributed.

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