Course Outline 2020-2021
Applied econometrics 2
In this course, we will study how econometric methods can help answer causal questions. We will discuss why establishing credible causal links is difficult in social sciences and how to overcome some of the challenges. We will build upon the tools introduced in applied econometrics 1 (linear regressions) to outline several methods central to modern econometric practice: experiments, instrumental variables, regression discontinuity designs, fixed effects estimations, differences-in-differences, event studies, matching and synthetic control. We will use relevant real-world examples to illustrate the assumptions and the limitations of each method. We will also learn about the experience of guest speakers who studied econometrics at the PhD level and now apply econometric tools in their daily (non-academic) jobs.
During the tutorials, students will work in group to critically assess research articles answering a causal question. They will proceed in five steps:
- Formulate a causal question and explain why it is interesting / important.
- Find two research articles using one of the methods studied in the course to answer the question.
- Discuss the methods used in each paper.
- Confront the results of both papers.
- Summarize the findings in a presentation and a term paper.
The intended learning outcomes of this course are the following:
- Explain why and when econometrics is useful
- Locate cutting-edge empirical economics research
- Assess the flaws and limitations of empirical work
- Evaluate firms’ decisions and public policies
- Marianne BLEHAUT (CREDOC)
- Georges Vivien HOUNGBONON (World Bank)
- Meryam ZAIEM (DARES)
The evaluation consists of three parts:
- Active participation during lectures and tutorials (individual grade - 20%)
- Oral presentation (individual grade – 30%)
- Term paper (group grade – 50%)
Slides are self-contained. Interested students can look at the following textbooks to find additional technical details and examples:
- Introduction to econometrics, Stock and Watson
- Mastering metrics, Angrist and Pischke
- Mostly harmless econometrics, Angrist and Pischke
In the applications we will discuss thoroughly the following articles:
- Bandiera O, Burgess R, Das J, Gulesci S, Rasul I and Sulaiman (2017) “Labor markets and poverty in village economies”, Quarterly Journal of Economics
- Duflo, E (2001) “Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment”, American Economic Review
- Ozier O (2017) “The Impact of Secondary Schooling in Kenya: A Regression Discontinuity Analysis”, Journal of Human Resources.
- Bleakley H (2003). “Disease and Development: Evidence from the American South.” Journal of the European Economic Association.
- Acemoglu, D and S Johnson (2007). “Disease and Development: The Effect of Life Expectancy on Economic Growth”, Journal of Political Economy.
Parcours de rattachement
Pour les étudiants du diplôme Diplôme d'ingénieur de l'Ecole polytechnique
Vous devez avoir validé l'équation suivante : UE ECO552
Format des notesNumérique sur 20Littérale/grade réduit
Pour les étudiants du diplôme Diplôme d'ingénieur de l'Ecole polytechniqueLe rattrapage est autorisé (Note de rattrapage conservée)
- Crédits ECTS acquis : 5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Economics, Data Analytics and Corporate FinanceL'UE est acquise si note finale transposée >= C
- Crédits ECTS acquis : 4 ECTS
Pour les étudiants du diplôme Economics for Smart Cities and Climate PolicyL'UE est acquise si note finale transposée >= C
- Crédits ECTS acquis : 4 ECTS