Topics in Microeconometrics, June 1-3, 2011

Topics in Microeconometrics

Professor William Greene

Stern School of Business, New York University

at

National University of Ireland, Galway

June 1-3, 2011

Professor

Dr. William Greene e-mail: wgreene@stern.nyu.edu

Professorís Home Page: http://www.stern.nyu.edu/~wgreene

Course Home Page: http://pages.stern.nyu.edu/~wgreene/NUI2011.htm

Abstract

This course will introduce the student to methods and models used to analyze cross section and panel data. We will depart from the linear regression model to specifications for binary and censored data, ordered choices, count data and multinomial choices. The discussion will present basic models for cross section data then introduce theory and methods for extensions to panel data and stated choice experiments.

The presentation, over three days, will include roughly ten morning classroom meetings. In the afternoon of each day, we will do some hands on analysis using "live" data sets and a familiar computer package.

The course will be focused on models and methods. We will do some computation to illustrate some of the model specifications. However, the course is not intended to be a "how to" for using specific techniques with a particular program (e.g., "How to analyze panel data with ____.")

Course Outline

Overview

This is a course in econometric analysis of cross section and panel data. There are a huge variety of models that are used in this context. We will focus on five which arguably comprise the foundation for the area: the linear regression model, 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, and a sixth, the stochastic frontier model, that is the foundation of a large and growing specialty in microeconometrics. Discussions will cover the topics listed below.

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 using a textbook such as Greene, W., Econometric Analysis, 6th edition.

Resource

No specific textbook is assigned for the course. Useful references are:

Greene, W., Econometric Analysis, 6th Ed., Prentice Hall, 2008 or 7th Ed., 2011

Cameron, A.C. and P. Trivedi, Microeconometrics: Methods and Applications, Cambridge University Press, 2005.

A recently published reference for some of the discrete choice models is

Greene, W. and D. Hensher, Modeling Ordered Choices, Cambridge University Press, 2010.

A lower level textbook that discusses some of the topics we will visit is

Wooldridge, J., Introduction to Econometrics: A Modern Approach, Southwestern, 2008

Some of the presentation will be based on Econometric Analysis, 7th ed., by Greene, W. (Prentice Hall, 2003). Six chapters are included with the course materials:

Chapter 11: Models for Panel Data

Chapter 12: Estimation Methods

Chapter 14: Maximum Likelihood

Chapter 15: Simulation Based Estimation and Inference

Chapter 17: Models for Discrete Data

Chapter 25: Models for Unordered and Ordered Discrete Choices

Topics

1. Linear Regression                                                   6. Censoring and Two Part Models

Descriptive tools                                                    Tobit model

Specification analysis                                           Truncation and censoring

Estimation and inference                                   Hurdle models and zero inflation

 

2. Endogeneity                                                             7. Nonlinear Panel Data Models

IV and 2SLS estimators                                        Binary choice

Control functions                                                  Clustering

Sample selection                                                   Random effects, fixed effects

Propensity score matching                                Dynamic models

 

3. Panel Data                                                                  8. Hetrogeneity and Mixed Models

Pooled data, clustering                                       Latent class modelling

Difference in differences                                   Random parameters

Fixed effects, FEVD, random effects

Mundlak specification

 

4. Binary Choice Models                                           9. Multinomial choice models

Nonlinear models and means                          Multinomial logit

Binary choice                                                           Specification and estimation/inference

Partial effects and interactions                        Extensions, nested logit, heterogeneity

Estimation and inference                                   Random parameter models

Endogenous RHS variables                                 Stated preference data

 

5. Ordered Choices and Count Data                     10. Stochastic Frontiers

Ordered choice models                                      Normal-half normal model

Fits, estimation, inference                                Estimating inefficiency

Models for count data                                         Panel data models

Overdispersion, negative binomial

The course outline can also be downloaded HERE

The schedule for the course will be as follows;

9.00 - 10.30       Session 1

10.30 - 10.50     Break

10.50 - 12.30     Session 2

12.30 - 13.30     Lunch

13.30 - 15.00     Session 3

15.00 - 15.20     Break

15.20 - 17.00     (or so) Lab