This course will survey
techniques used in modeling discrete data. Discrete choice models have become
an essential tool in modeling individual behavior. The techniques are used in
all social sciences, health economics, medical research, marketing research,
transport research, and in a constellation of other disciplines. This course
will examine several models and techniques used in these studies. We will begin
with a brief review of regression modeling concepts, then
turn to the fundamental building block in discrete choice modeling, the binary
choice model. Several variants and extensions will be discussed before we turn
attention to ordered choice models and models for counts. The second half of
the course will be devoted to multinomial choice models of the sort used, e.g.,
in modeling brand choice in marketing, travel mode choice in transport, and a
huge variety of applications in the social and behavioral sciences. Students in
this course will obtain background in both the theory and methods of estimation
for discrete choice modeling. This course will provide a gateway to the
professional literature.
This is a course
in econometric analysis of discrete data. There are a huge variety of models
that are used in this context. We will focus on four which arguably comprise
the foundation for the area: the fundamental model of binary choice (and a
number of variants); models for ordered choices; the Poisson regression model
for count data; and the fundamental model for multinomial choice, the
multinomial logit model. Discussions will cover the topics listed below.
Prior knowledge is assumed to
include calculus at the level assumed in the first year of a Ph.D. program in
economics and a course in econometrics at the beginning Ph.D. level using a
textbook such as Greene, W., Econometric
Analysis, 6th edition.
No specific textbook is assigned
for the course. A useful reference is
Applied Choice Analysis by Hensher, D., Rose, J. and Greene, W., (Cambridge University Press,2005).
Some of the presentation will be
based on Econometric Analysis, 6th
ed., by Greene, W. (Prentice Hall, 2003). Four chapters are included with
the course materials: Left click to
activate. Right click to download
Chapter 14: Estimation Methods Greene-Chapter-14.pdf (Estimation
methods)
Chapter 16: Maximum Likelihood Greene-Chapter-16.pdf (Maximum
likelihood estimation)
Chapter 17: Simulation Based Estimation and Inference Greene-Chapter-17.pdf (Simulation
based estimation)
Chapter 23: Models for Discrete Data Greene-Chapter-23.pdf (Discrete
choice models)
Chapter 25: Models for Count Data and Duration Greene-Chapter-25.pdf (Count data
models)
The received literature on
discrete choice models is vast – one could easily compose a list of
thousands of articles. Your course materials include a small handful of
articles, mainly for the purpose of illustrating particular techniques or
ideas. (Working papers are attached. Most of these have been published
elsewhere.) These are:
“Economic Choices,” American
Economic Review, McFadden, D. (2001). McFadden’s
Nobel Prize lecture.
“Mixed
MNL Models for Discrete Response,” McFadden, D. and Train, K., Journal of Applied Econometrics, 2000.
“The Behaviour of the Maximum Likelihood Estimator of Limited
Dependent Variable Model in the Presence of Fixed Effects,” Greene, W., Econometrics Journal, 2004.
“Discrete Choice Models,” W. Greene, (a survey of
discrete choice models) forthcoming, Palgrave
Handbook of Applied Econometrics, 2009.
“Functional Form and Heterogeneity
in Models for Count Data,” W. Greene, (a survey of models for count
data); Foundations and Trends in
Econometrics, 2007..
“Modeling Ordered
Choices,” (W. Greene, 2008) A survey of the
literature on ordered choice models.