Objectifs

See the english description

Contenu

This class will cover different topics, giving the students the opportunity to be in touch with many different econometric specifications, depending on the data they have to handle.The material not covered during the lectures is not part of the final exam.

1) Refresher on linear models

2) Endogeneity

3) Causal inference models

4) GMM and Maximum Likelihood estimation frameworks

5) Poisson models

6) Panel data models

7) Qualitative dependent variables

Exercices

6 sessions of 2 hours each will be organized to help students to apply the theoretical concepts seen in class on real data using the Stata software. 

These sessions will be monitor by 2 different teaching assistants.

FInally, these sessions will be used by students to discuss the homework they will have to present at the end of the class.

Méthodes d'enseignement

The class consists in weekly lessons of 2 hours each. The content of the lectures will be posted ahead of schedule  on webcampus.

Besides, 6 sessions of 2 hours each will be monitor by 2 teaching assistants. The schedule of these applied sessions will be posted on

 
 
 

 

 

Méthode d'évaluation

For the first session exam, the evaluation consists of two parts. 1) A homework in which the student needs to define a research question, find an appropriate database and apply the appropriate techniques to answer the research question (30%).

2) A written exam (70%).

For the september session, the final grade will only consist of the grade of the final exam

 

Rules concerning the use of Artificial Intelligence tools
This course sticks to three out of the four key principles set out in the UClouvain note on the use of AI tools:
 
 - Principle of responsibility: Students are entirely responsible for the work they submit. They must be the result of a personal process.
- Principle of transparency: Where necessary, students should clearly indicate the aids and tools used. Such use must respect the principles of academic integrity.
- Principle of authenticity: Students must ensure that their work enables an assessment of the knowledge and skills they have authentically acquired. This applies both to the result of their work and to the process and method used to produce it.

For this course, you're only allowed to use AI tools to translate your homework to english and/or improve the english writing. If you do so, you need to explicitly mention the use of AI tools (see the UCLouvain note for more details).

 

Sources, références et supports éventuels

The class borrows material from several textbooks. The main ones are

Wooldridge, J., 2010, Econometric Analysis of Cross section and Panel Data, The MIT Press, London

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

The support material will consists of the pdf slides posted on Webcampus.

Langue d'instruction

Français