See the english description
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) GMM and Maximum Likelihood estimation frameworks
4) Qualitative dependent variable
5) Non-negative dependent variable
6) Panel data
7) Causal inference models
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 a teaching assistant.
These sessions can be used by students to discuss the homework they will have to present at the end of the class.
See the english section
For the first session exam, the evaluation consists of two parts. 1) Students will be required to prepare a written report and deliver a presentation on a scientific paper selected from a list provided.(35%).
2) A written exam (65%).
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).
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
More references will be presented in the slides.
The support material will consists of the pdf slides posted on Webcampus.
| Formation | Programme d’études | Bloc | Crédits | Obligatoire |
|---|---|---|---|---|
| Master 120 en sciences économiques, orientation générale, à finalité didactique | Standard | 0 | 5 | |
| Master 120 en sciences économiques, orientation générale, à finalité spécialisée | Standard | 0 | 5 | |
| Master 120 en sciences économiques, orientation générale, à finalité approfondie | Standard | 0 | 5 | |
| Master 120 en sciences économiques, orientation générale, à finalité didactique | Standard | 1 | 5 | |
| Master 120 en sciences économiques, orientation générale, à finalité spécialisée | Standard | 1 | 5 | |
| Master 120 en sciences économiques, orientation générale, à finalité approfondie | Standard | 1 | 5 |