Learning outcomes

At the end of the class,  students will be able to distinguish between econometrics methods dealing with different types of dependent variables (continuous, discrete, strictly positive, repeated over individuals).

Secondly, they will be able to estimate and interpret these models using Stata software.

Finally, they will be familiarized with the treatment effect literature and will be able to estimate such models under different sets of assumptions about the treatment effect.

 

Goals

This class aims to familiarize the students with the treatment of various types of datas. As such, different econometrics techniques will be presented, covering cross-sectional and panel data. Besides, we will also cover qualitative dependent variables, which require particular econometric modelling and interpretations.

A second objective is to allow students to be autonomous in the analysis of these data. To achieve this, tutorials, monitored by 2 teaching assistants will be organized. These sessions will focus on applying the methods seen in class on real data using the Stata software. Besides, they can serve as discussion room for the homework that has to been handed at the end of the period.
 
The topics that are covered are a combination of subjects typically covered in introductory and more advanced courses in econometrics. While some background in econometrics is helpful, the course is also open to students without prior knowledge of econometrics (but willing to study).



 

Content

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

 

 

Table of contents

See the content part.

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 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.

 

Teaching methods

The course consists of lectures and tutorials. During the tutorials, the econometric methods are further illustrated through examples (typically based on research papers) and you have the opportunity to make exercises based on these examples. We use the software Stata during tutorials.


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 monitored by a teaching assistant. The objective of these sessions is to apply the econometrics methods seen in class to Stata. The schedule of these applied sessions will be posted on

https://horaires.unamur.be


For the homework (prepare a written report and a presentation of an article), instructions will be given in class and posted on the website of the course (https://webcampus.unamur.be/)

 

Assessment method

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).

Sources, references and any support material

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.

 

Language of instruction

English