Descriptif
Introduction to Econometrics (ECO203) introduces the most common ways to study and analyze economic data, with a focus on emphasizing data analysis for empirical causal inference. Topics include linear regressions, randomized trials, instrumental variables, and differences-in-differences. Students also learn how to study datasets through practical examples (R coding).
Textbook: Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge / Basic Econometrics by Damodar N. Guarati and Dawn C. Porter.
Objectifs pédagogiques
The objective of this course is to introduce students to simple and multiple regression methods for analyzing data in economic fields. Students learn how to analyze and interpret empirical results. Students should gain an intuitive understanding of the principles of econometric analysis and apply them to data.
Diplôme(s) concerné(s)
Parcours de rattachement
Pour les étudiants du diplôme Bachelor of Science de l'Ecole polytechnique
Vous devez avoir validé l'équation suivante : UE ECO_1F001_EP Et UE ECO_1S002_EP
Format des notes
Numérique sur 20Littérale/grade américainPour les étudiants du diplôme Bachelor of Science de l'Ecole polytechnique
Vos modalités d'acquisition :
Final exam (2h, written) without material (50%), and two homeworks (50%)
Remedial exam: Oral, 1h without material
Le rattrapage est autorisé (Note de rattrapage conservée écrêtée à une note seuil de 10)- Crédits ECTS acquis : 5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Programmes d'échange internationaux
Vos modalités d'acquisition :
Final exam (2h, written) without material (50%), and two homeworks (50%).
Remedial exam: Oral, 1h without material
Le rattrapage est autorisé (Note de rattrapage conservée écrêtée à une note seuil de 10)- Crédits ECTS acquis : 5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Programme détaillé
1. Introduction to econometrics (1h30 for theory and 2h for exercises)
2. Simple regression analysis (3h for theory and 4h for exercises)
3. Multiple regression analysis: Statistical properties, inference, asymptotics, qualitative information (9h for theory and 12h for exercises)
4. Pooled cross sections and di fference-in-diff erences (3h for theory and 4h for exercises)
5. Advanced panel data methods (fixed effects, random effects,...) (1h30 for theory and 2h for exercises)
6. Instrumental variables estimation and two-stage least-squares (1h30 for theory and 2h for exercises)
7. Local average treatment effects (LATE) (1h30 for theory and 2h for exercises)