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Discrete Choice Models

This module covers the estimation and usage of discrete choice models that are increasingly estimated using simulation methods. Discrete choice models are used to examine the choices of individual consumers, households, firms and other agents. The module covers the main discrete choice models and a variety of specifications that build on these models, as well as standard maximum likelihood and simulation-based estimation techniques. Discrete choice models are applicable in many fields, including energy, environmental studies, health, labour, marketing, urban economics and transportation. This module can be taken on its own, or as part of a PG Certificate, PG Diploma or Full Masters Programme.

Prerequisites

Please note to take this course you must first have completed Foundations of Econometrics.

Delivery

Two-hour lectures per day over five days, plus a one-hour tutorial each day, with optional clinics on the final day. Sessions are live online and recorded.

Study Hours

Approximately 100 hours of study, of which 15 are contact hours and 85 are private study.

Software Used

PcGive, R gets

Credits

10 CATS credits

Module Lead
Dr. Anthony Murphy

Dr. Anthony Murphy

Programme Faculty

Key Outcomes

  • —Non-linear binary models
  • —Probit models and estimation
  • —Logit models and estimation

Desired Skills

  • —Use automatic model selection to build econometric models
  • —Use saturation estimation
  • —Present, interpret and analyse the results of automatic model selection
  • —Utilise effectively PcGive and the R gets package

Lecture Programme

  1. WK 01

    Lecture 1: Properties of Discrete Choice Models

    • ·Properties of Discrete Choice Models
    • ·Conditional Logit Models
    • ·Estimation
  2. WK 02

    Lecture 2: Generalized Extreme Value

    • ·Generalized Extreme Value / Nested Logit Models
  3. WK 03

    Lecture 3: Probit Models

    • ·Probit Models
    • ·Drawing from Densities
    • ·Simulation Assisted Estimation
  4. WK 04

    Lecture 4: Mixed Logit Models

    • ·Mixed Logit Models
    • ·Individual-Level Parameters
    • ·EM Algorithm
  5. WK 05

    Lecture 5: Other Issues

    • ·Other Issues - Stated vs Revealed Preferences, Ranked Data, Ordered Responses, Contingent Valuation, Dynamic Optimization
    • ·Endogeneity

Assessment

Online coursework submission with the support of your module lecturer and tutor. All sessions are conducted live online at a scheduled time and recorded for review.

Build your qualification

Take Discrete Choice Models as a standalone module, or as part of a buildable MSc.