## Module 6500 in Bonn

Advanced Applied Econometrics 2:

Limited Dependent Variable (LDV) / Choice models

### Lecturers

Prof. Dr. Thomas Heckelei, thomas.heckelei@ilr.uni-bonn.de

Prof. Dr. Silke Hüttel, S.Huettel@ilr.uni-bonn.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

I 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

II 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

III Multiple Choice Models

- Ordered Response Models (Verbeek Ch. 7.2, Greene Ch. 18.3)
- Introduction
- The ordered probit model
- Estimation, Effects and Interpretation
- Testing
- 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

IV Count data models (Verbeek Ch. 7.3)

- General motivation
- Poisson model
- Negative Binomial Model
- Illustrations

V Tobit models (Verbeek Ch. 7.4-7.5)

- Tobit I
- Utility maximisation problem
- Standard Tobit model
- Tobit estimation
- Tobit specification test
- 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**

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