William Greene
Stern School of Business
New York University
Info-Metrics Workshop
American University
Cross Section and Panel Data Modeling
May 14-18, 2012
Professor
Professor William. Greene
e-mail: wgreene@stern.nyu.edu
Home Page: http://www.stern.nyu.edu/~wgreene
Curriculum
for American University, Info-Metrics Workshop, May 14-18, 2012
Day
1 (Monday May 14) : Methodology and Regression Basics
o Introduction
o Session 1 Methodology . Descriptive Statistics and Linear
Regression Endogeneity
o Session 2 Quantile Regression and Bootstrapping
Linear Regression with Panel Data
o Session 3 Linear Regression with Panel Data, Random Parameter and Hierarchical Linear
Models
Day
2 (Tuesday May 15): Nonlinear Models and Discrete Choice
o Session 1 Binary Choice Estimation
o Session 2 Binary Choice Inference
o Session 3 Panel
Data Models for Binary Choice
Day
3: (Wednesday May 16) Discrete Choice Models and Heterogeneity in Panel Data
o Session 1 Extended Models for
Binary Choice,
o Session 2 Ordered Choices
o Session 3 Count Data, Modeling
Heterogeneity
Day
4: (Thursday May 17) Multinomial Choice
o Session 1 Multinomial Logit,
o Session 2 Multinomial Logit
Extensions, Nested Logit Model,
Modeling Heterogeneity
in Multinomial Choice Models
o Session 3 Random Parameter and Latent Class Models,
Part 13: Latent Class
Models
Day 5:
(Friday May 18) Topics in Choice Modeling
o
Session 1 Mixed Logit Models,
Stated Preference Data
o Session 2 Discrete Choice Models for Spatially
Correlated Data
o Session 3 Sample Selection in Nonlinear Models
(And topics to be determined.)
Left click to open. Right click to download.
o
Assignment 1: Basic
Regression (NLOGIT Commands for
Assignment 1)
o
Assignment 2: Binary
Choice Modeling (NLOGIT Commands for
Assignment 2)
o
Assignment 3: Ordered
Choice Models (NLOGIT Commands for
Assignment 3)
o
Assignment 4:
Multinomial Logit Model (NLOGIT Commands for
Assignment 4)
o
Assignment 5: Student
Project: Model for Moral Hazard (NLOGIT Commands for
Assignment 5)
Abstract
This course will survey
techniques used in modeling cross section and panel data. Emphasis will be on
discrete data, though results and techniques are mostly generic and will extend
to other modeling frameworks. 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
a large number of 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 multiple equation binary choice models, 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.
The course will include lectures
that develop the relevant theory and extensive practical, laboratory
applications. Emphasis in the laboratory sessions will be on estimation of
discrete choice models and using them to describe behavior and to predict
discrete outcomes. Course participants will apply the techniques on their own
computers using the NLOGIT computer
program and several real data sets that have been used in applications already
in the literature.
Prerequisites
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
out of a textbook such as Greene, W., Econometric
Analysis, 7th edition. Familiarity with NLOGIT will be helpful, but is not necessary. Two useful reference
books for the course are the primer Applied
Choice Analysis by David Hensher, John Rose and William Greene (Cambridge
University Press, 2005) and the survey monograph, Modeling Ordered Choices by William Greene and David Hensher
(Cambridge University Press, 2010). A somewhat more advanced than needed for
our purposes, but comprehensive and up to date survey of econometric theory is
Wooldridge, J., Econometric Analysis of
Cross Section and Panel Data, 2nd ed., MIT Press, 2010.
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 as well as practical applications of the methods at the
level of the contemporary research in the field. Emphasis in the course is on
applications of methods. Derivations of, e.g., asymptotic properties of
estimators, and theoretical fine points, such as the implications of different
types of independence assumptions in panel data models are left for more
advanced treatments. This course is intended as a bridge to the literature and
a practical application of some widely used techniques.
Course
Outline
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. The course will consist of discussions and laboratory
sessions which will apply the techniques to live data sets and some time devoted
to topics, discussions and laboratory work on student projects. Discussions
will cover the topics listed below. Lab sessions will apply the techniques
discussed in the preceding sessions. Practicals will consist of directed
exercises and student assignments to be completed singly or in groups.
No specific textbook is assigned
for the course. Some of the presentation will be based on Econometric Analysis, 7th ed., by Greene, W. (Prentice
Hall, 2012). 6 chapters are included with the course materials:
Left click to open. Right click to download. (These are PDF
files.)
Greene 11-Panel data
methods
Greene 12-Estimation
methods
Greene 14-Maximum
likelihood estimation
Greene 15-Simulation
based estimation and inference
Greene 17-Discrete
choice models
Greene 18-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., Econometric Journal, 2004.
Discrete
Choice Models, W. Greene, (a survey of discrete choice models), 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, Greene, W. and Hensher, D., Cambridge
University Press, 2010.
Articles about discrete choice modeling:
Left click to
open. Right click to download. (These are PDF files.)
CountDataSurvey.pdf
DiscreteChoiceSurvey.pdf
OrderedChoiceSurvey.pdf
FixedEffects.pdf
McFadden-EconomicChoices.pdf
McFadden-Train.pdf
I. Class Notes: These are
Powerpoint slide presentations for use during the class sessions. Topics 1-7
discuss general results for some of the discrete choice modeling frameworks.
Session
_________________________________________________________________________________________________________
Introduction: Course
description and overview
1: Methodology,
software, modeling concepts, regression basics
Left click to open. Right click to download. (These are
.pptx files)
Microeconometrics
Topics 1. Descriptive Statistics and Linear Regression
Microeconometrics
Topics 2. Endogeneity
Microeconometrics
Topics 3. Linear Regression with Panel Data
Microeconometrics
Topics 4. Quantile Regression and Bootstrapping
2: Standard
models for binary choice
Microeconometrics
Topics 5. Bayesian Analysis
Microeconometrics
Topics 6. Nonlinear Models
3: Analysis
of binary choice. marginal effects, fit measures, prediction, hypothesis tests
4: Bivariate
probit models, simultaneous equations, sample selection, multivariate probit
model marginal effects, prediction and analysis of bivariate choices
Left click to open. Right click to download. (These are
.pptx files)
Microeconometrics
Topics 7. Sample Selection in Nonlinear Models
5: Panel data
models for binary choice, random effects, fixed effects, Mundlak formulation,
incidental parameters problem, dynamic probit model
Left click to open. Right click to download. (These are
.pptx files)
Microeconometrics
Topics 8. Random Parameter and Hierarchical Linear Models
6: Panel
data, heterogeneity, simulation and latent class models
Left click to open. Right click to download. (These are
.pptx files)
Microeconometrics
Topics 9. Some Latent Class Models
Microeconometrics
Topics 10. Discrete Choice Models for Spatially Correlated Data
7: Ordered
choice models, ordered outcomes, estimation and inference, generalized models,
recent developments, models for counts
8: Models for count data
9: The
multinomial logit model, random utility models, IIA, logit modeling. fit and
prediction, marginal effects, model simulation
10: Extensions
of the MNL model, multinomial probit, heteroscedasticity, model simulation, use
of the MNL model
11: Nested
logit model and generalized nested logit models,
12: Modeling heterogeneity of
preferences, simulation based estimation, error components
13. Latent class models
14. Mixed
logit and random parameter models, simulation, generalizations of the mixed
logit model
15: Repeated
observations, panel data, revealed vs. stated preference data
End: Observations and
closing remarks
Left click to open. Right click to download. (These are
.pptx files)
Part 0: Introduction
Part 1: Methodology
Part 2: Binary Choice Estimation
Part 3: Binary Choice Inference
Part 4: Extended Models for Binary
Choice
Part 5: Panel Data
Models for Binary Choice
Part 6: Modeling
Heterogeneity
Part 7: Ordered Choices
Part 8: Count Data
Part 9: Multinomial Logit
Part 10: Multinomial Logit Extensions
Part 11: Nested Logit Model
Part 12: Modeling Heterogeneity in
Multinomial Choice Models
Part 13: Latent Class Models
Part 14: Mixed Logit Models
Part 15: Stated Preference Data
II. Assignments: Exercises
and Practicals for Discrete Choice Modeling.
Note there are two parts to each, the .pdf file for the
assignment and the .lim file that contains the NLOGIT commands.
Left click to open. Right click to download.
Assignment 1: Basic
Regression (NLOGIT Commands for
Assignment 1)
Assignment 2: Binary
Choice Modeling (NLOGIT Commands for
Assignment 2)
Assignment 3: Ordered
Choice Models (NLOGIT Commands for
Assignment 3)
Assignment 4:
Multinomial Logit Model (NLOGIT Commands for
Assignment 4)
Assignment 5: Student
Project: Model for Moral Hazard (NLOGIT Commands for
Assignment 5)
III. Lab Notes: These are
Powerpoint slide presentations that explain using NLOGIT and how to do the
assignments.
Left click to open. Right click to download.
Lab 1: Getting Started
Lab 2: Binary Choice
Lab 3: Ordered Choice and Count Data
Models
Lab 4: Multinomial Choice
IV. Data Sets: These are
NLOGIT project (.lpj) files.
Left click to open. Right click to download.
American Express
Credit Data
Spector and Mazzeo
Basic Probit Data
Shoe Brand Choices,
Stated Preference, Multinomial Choice
Multinomial Choice,
Travel Mode
Combined Travel Mode and Brand Choices Data. (Use this for Assignment 4.)
Dairy Farms, Panel
Data
Health Care Panel
Data
Labor Supply Data
(Mroz Study)
Panel Data on
Innovation for Probit Models
Stated Preference
Data on Automobile Choices
V. NLOGIT Software: This
section contains a brief introduction and two manuals: The short introduction
is a “getting started.” The LIMDEP manual explains the basics of using LIMDEP
and NLOGIT. (LIMDEP is embedded in NLOGIT). The NLOGIT manual contains
descriptions of how to use the special features for discrete choice modeling
with NLOGIT (as well as some additional material on other discrete choice
models that are also contained in LIMDEP). The setup file contains an
installation kit for installing a copy of NLOGIT made specially for this course
on your own computer. You should download the setup file to your own computer
and execute it there, rather than launching it from your web browser.
Left click to open. Right click to download.
Short Introduction to
NLOGIT
LIMDEP Student User’s
Manual
NLOGIT Student User’s
Manual
NLOGIT Software Setup
for Installing NLOGIT on Your Computer