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, s.hirsch@uni-hohenheim.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

Teaching forms	Workload (h)
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