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Discrete Choice Modeling - Theory
and Practice
Instructor: William
Greene, New York University
DATES: MAY 30 - JUNE 3, 2006
LOCATION: AMERICAN UNIVERSITY
Objectives and Scope
Discrete choice models can now be found in practice in all the
social sciences, in medical research, transport research, the
physical sciences, and throughout the research environment where
the behavior of individual entities and decision makers is analyzed
empirically. The purpose of this class is to provide the background
for understanding both the underlying theory and the empirical
tools for practical application of discrete choice modeling in
a variety of economic/econometric estimation problems. We will
survey the broad scope of discrete choice models.
The model building aspect of the course will begin with the rudiments
of multiple regression, then quickly proceed to the fundamental
building block, the canonical model of binary choice. From there
we will proceed to many extensions including the multinomial logit
model (MNL) for choice among multiple alternatives. Variants on
the MNL will include recent advances in mixed logit (random parameters),
nested logit, and so on, for models that are designed to accommodate
a wide variety of behavior patterns. Applications will be drawn
from economics, marketing and transport research.
We will also examine basic and advanced models for binary choice,
including models for sample selection, censoring, and so on. Applications
here are drawn from economics and health economics. A third set
of results is built around observations on count data, such as
frequencies of bankruptcies, accidents, health care utilizations,
recreation site usage, etc. Here, we will visit a large variety
of specifications, particularly those designed to handle stratification,
censoring, truncation and selection.
Recent work in many fields has focused on panel data models. We
will examine many different extensions of the discrete choice
models to panel data. The course will examine familiar, basic
methods and frontier developments in the field.
In addition to the theoretical, model developments, students will
spend a large amount of the course time and effort on received
studies from the literature and, most importantly, on hands on,
empirical applications using ‘live’ data sets. Students
will be introduced to NLOGIT, one of the most widely used packages
for discrete choice models. Part of the course work will consist
of practical applications and development of a model to be discussed
on the last day of class.
Preliminary time schedule
Each day will consist of four sessions, two morning and two afternoon.
Within each interval, we will spend one of the two sessions on
discussing the models and empirical applications. A second session,
one in the morning and one in the afternoon, will be spent in
the computer lab, where students will learn how to use NLOGIT
and will estimate models and do empirical analysis of ‘real’
data sets.
Content and Topics
Below is a tentative topical outline. The order of topics may
change. Depending on availability of time, and interest, other
topics may be included and some topics may not be covered. In
each of these sections, we will consider underlying theory, specific
models, estimation, and received applications.
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.
Target Group and Requirements
The course may be of interest to
1. PhD students interested in new methods of estimation. Students
from American University and other universities are welcome.
2. Faculty, professional economists, researchers and econometricians
who work in support of decision making in government agencies
as well as the private market.
NOTE: Participants should have prior knowledge at the level of
an introductory course econometrics or a course in statistical
analysis (estimation techniques) at the PhD level.
Credits
The course can be taken for three credits or for no credit. To
receive the full three credits, the participant needs to complete
a research paper. Credits can only be obtained by writing the
applied paper. Students can start working on the paper at the
end of the course. The paper is due a number of weeks after the
end of the sessions.
Materials
• The main texts are
Hensher, D., Rose, J. and Greene, W., Applied Choice Analysis,
Cambridge University Press, 2005 and Cameron, A.C. and
Trivedi, P., Microeconometrics, Cambridge University Press, 2005.
• A Reader composed of
the main readings for the class will be provided to each participant.
This will include selections from Greene, W., econometric
Analysis and some articles.
• A detailed reading
list will be provided with the Final Syllabus.
• Handouts for NLOGIT
will be provided as well.
Costs
Three Credit costs for students.
Fixed fee (zero credits) for Researchers.
Location
American University, Washington, D.C.
Registration
Download the necessary document/s from the main
page and submit to the Economics department, or look at: http://www.american.edu/american/registrar/
About the Instructor
William Greene is currently Professor of Economics and Entertainment
and Media Faculty Fellow, Department of Economics, Stern School
of Business, New York University. His primary field of interest
is applied econometrics in the areas of discrete choice modeling,
production economics and efficiency estimation and panel data.
He is also a student of the economics of the entertainment industry.
Teaching at New York University since 1982 includes Econometrics,
Microeconomics, Macroeconomics and Economics of the Entertainment
and Media Markets. Previously Professor of Economics, Cornell
University, 1976-1982. Other employment includes visiting lectureships
at Oxford, Indiana, University of York, the Chicago Federal Reserve
Bank, University of Lund, and consulting to The World Health Organization,
Resource Consultants, Inc., Ortho Biotech, National Economic Research
Associates, The U.S. Navy and others. He is the president of Econometric
Software, Inc.since 1985. He is the author of the textbooks Econometric
Analysis and Applied Choice Analysis, software LIMDEP and NLOGIT,
and approximately 100 articles in applied econometrics, econometric
methods, transportation, health economics, etc. appearing in Econometrica,
Journal of Econometrics, Review of Economics and Statistics, Transport
Research, Health Economics, The Journal of Productivity Analysis,
Econometric Reviews, Empirical Economics, Journal of Political
Economy, Journal of Economic Education, Economic Perspectives
and the American Economic Review and others. He is currently the
editor in chief of Foundations and Trends in Econometrics and
is an associate editor for The Journal of Productivity Analysis
and the Journal of Economic Education.
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