# Module 6500 Advanced Applied Econometrics 2: Limited Dependent Variable (LDV) / Choice models

## Lecturers

Prof. Dr. Thomas Heckelei,  thomas.heckelei@ilr.uni-bonn.de
Prof. Dr. Stefan Hirsch, stefan.hirsch@tum.de

### Aims

At the end of the course students shall

• understand the basics econometric methods and be able to apply these to real problems,
• understand, apply and interpret theory-based econometric models,
• be able to work with the econometrics package R

Skills: Methodological competence, quantitative analysis, conceptual thinking

### Contents

• Binary and multiple choice models
• Maximum Likelihood estimation
• Models for limited dependent variables
• Heckman-Models
• Empirical problems

## Outline

1. Maximum Likelihood Estimation (Verbeek Ch. 6.1 and 6.2
• Introduction to and examples for ML estimation
• General approach and estimation of the variance of the ML estimator
• Specification Tests
2. Binary Choice Models (Verbeek Ch. 7.1)
• Why not Linear Regression?
• Probit and Logit models
• Underlying latent variable model
• Estimation
• Goodness-of-fit
• Specification tests
3. Multiple Choice Models
1. Ordered Response Models (Verbeek Ch. 7.2, Greene Ch. 18.3)
• Introduction
• The ordered probit model
• Estimation, Effects and Interpretation
• Testing
2. Multinomial Models (Verbeek Ch. 7.2, Greene Ch. 18.1-18.2)
• Introduction, formalization, distributions
• The conditional logit model: introduction, estimation and interpretation
• The multinomial logit model: introduction, estimation and interpretation
• Independence of irrelevant alternatives IIA assumption
• The nested logit model: : introduction, estimation and interpretation
4. Count data models (Verbeek Ch. 7.3)
• General motivation
• Poisson model
• Negative Binomial Model
• Illustrations
5. Tobit models (Verbeek Ch. 7.4-7.5)
1. Tobit I
• Utility maximisation problem
• Standard Tobit model
• Tobit estimation
• Tobit specification test
2. Tobit II (Double Hurdle)
• Statistical model
• Heckmann selection model
• Estimation
1 week block seminar with lecture and exercise 40
Exercises 50
Total 90

Examination: Exercises

Grading: In-class and homework assignments, where a minimum score of 50% is required to pass the module.

Credit points: 3 CP

### Requirements: Basics in econometric course or module 2500 AAE-1: Linear Models and Panel Data or module 7600 Applied Microeconometrics and Impact Analysis

Language: English

### Literature

• Greene, W. (2012): Econometric Analysis, 7th edition. Pearson
• Verbeek, M. (2012): A Guide to Modern Econometrics, 4th edition. John Wiley & Sons

### Software: R

During the course all exercises can be conducted in R or Stata, while support will be given in R only and our handouts will be in R. If R is new to you please familiarize yourself with basics in R.
http://www.r-tutor.com/r-introduction
https://cran.r-project.org/doc/contrib/Torfs+Brauer-Short-R-Intro.pdf