Course teacher(s)
Elise Petit (Coordinator) and Vincenzo VERARDIECTS credits
5
Language(s) of instruction
english
Course content
In this course we cover a wide range of models.
The plan of the course is the following:
- Introduction and refresher
- Panel data (2 lectures)
- Time series (2 lectures)
- Instrumental variables
- Semiparametric and robust estimators
- Qualitative and dependent variable models (2 lectures)
- Limited dependent variable models
- Small samples
Objectives (and/or specific learning outcomes)
The objective is to learn to use econometric models in practice: which model to use in which situation, under which assumptions, and what the results tell us.
A particular emphasis is put in understanding the characteristics of the data and consequently learn how to choose the most appropriate model to use.
For each model, the theory is revised but without going into details. The applied dimension of the problem is systematically priviledged, as well as the nuanced intepretation of its results.
Causal inference is introduced in several chapters.
Some basic notions of programming are taught during the exercise sessions.
Prerequisites and Corequisites
Required and Corequired knowledge and skills
Undergraduate courses in Mathematics, Statistics and Econometrics
Teaching methods and learning activities
The main lectures are complemented by two types of emprical sessions: 6 sessions around the group project, and 6 regular TP sessions to help understand the material covered in the lectures. All the exercises are in Stata software (but students can use R if they want).
References, bibliography, and recommended reading
The main reference books are
1. Dougherty, C. (2011). Dougherty: Introduction to Econometrics. Oxford University Press <- Many slides are based on the slides companion to this book !!
2. Baltagi, B. (2005). Econometric Analysis of Panel Data, Wiley
3. Cameron, A. and Trivedi, P. (2005). MICROECONOMETRICS: Methods and Applications. Cambridge University Press
4. Cameron, A. and Trivedi, P. (2010). MICROECONOMETRICS USING STATA. Stata Press
5. Greene, W. (2011). Econometric Analysis, Prentice Hall
6. Wooldridge, J. (2000), Introductory Econometrics: A Modern Approach. South-Western.
7. Wooldridge, Jeffrey M. (2001) Econometric Analysis of Cross Section and Panel Data. MIT Press.
Contribution to the teaching profile
This course contributes to the following programme objectives:
LO 1.1 - Integrate sustainable development into problem analysis
LO 1.2 - Identify and apply the relevant analytical tools and scientific knowledge to analyse an economic problem in depth
LO 1.4 - Collect, organise and analyse data to critically understand the main policy challenges and to support policy analysis and recommendations
LO 2.1 - Adopt a scientific approach to data collection, research and analysis and communicate results with clear, structured and sophisticated arguments.
LO 2.2 - Display critical thinking and develop autonomous learning strategies and techniques
LO 3.1 - Apply quantitative and qualitative techniques to support analysis using data with standard office and statistical software
LO 4.1 - Work and communicate effectively as part of a team in an international and multicultural environment
Other information
Contacts
elipetit@ulb.be
Campus
Solbosch
Evaluation
Method(s) of evaluation
- Practice work
- written examination
Practice work
written examination
The exam is written. Questions are both theoretical and empirical. An individual cheatsheet of handwritten notes (2-sided A4) is allowed during the written exam.
Part of the grade comes from the group project (facultative). Students who fail in January can maintain their group project grade in August, or next year.
The course instructor reserves the right to invite any student to an additional oral examination in order to verify the student’s personal mastery of the content of the submitted examination, particularly when inconsistencies identified in the examination prevent the instructor from accurately assessing the student’s level of mastery of the knowledge and skills targeted by the course.
Mark calculation method (including weighting of intermediary marks)
Final grade is the higher of
- 100% exam grade, or
- 70% exam grade + 30% group project grade (this option is only available if the exam grade is > 7/20)
Language(s) of evaluation
- english