ECON0212-3

Duration

30h Th, 15h Pr

Number of credits

	Bachelor in economics and business management	4 crédits
	Bachelor in business engineering	4 crédits
	Master in economics : general (60 ECTS) (Réinscription uniquement, pas de nouvelle inscription)	5 crédits
	Extra courses intended for exchange students (Erasmus, ...)	5 crédits

Lecturer

Natalia Bermùdez

Language(s) of instruction

French language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Chapter 0: Introduction - Nature and approach of econometrics; types of data.
Chapter 1: Data analysis - Introduction to Stata; data management, visualization and descriptive analysis.
Chapter 2: The simple linear regression (SLR) model - Introduction to simple linear regression; ordinary least squares (OLS) estimation and interpretation.
Chapter 3: Causality - Distinguishing correlation from causation; selection bias; the potential outcomes framework and randomized controlled trials (RCTs).
Chapter 4: The multiple linear regression (MLR) model: estimation - Extension to several explanatory variables; OLS estimation and interpretation; categorical variables and omitted variable bias.
Chapter 5: The multiple linear regression (MLR) model: assumptions - Model assumptions and properties of the OLS estimators; homoskedasticity and heteroskedasticity.
Chapter 6: Linear regression: inference - Standard errors, confidence intervals and hypothesis tests in the regression model.
Chapter 7: Linear regression: extensions - Functional forms, transformations, interactions and other extensions of the linear model.
Chapter 8: Difference-in-differences - Introduction to the difference-in-differences econometric method; construction of the counterfactual, the parallel trends assumption and regression-based estimation.

Learning outcomes of the learning unit

By the end of this course, students will be able to:

manipulate, clean and visualize datasets using Stata, including its core commands for producing estimates and graphs;
explain, estimate and interpret simple and multiple linear regression models;
apply these tools to address simple empirical questions in economics and management;
distinguish correlation from causation, identify the main sources of bias in an econometric analysis, and discuss their limitations and possible remedies.

Prerequisite knowledge and skills

Basic mathematics: algebra and functions
Probability and descriptive statistics
Basic notions of statistical inference

Planned learning activities and teaching methods

Nine lectures on the topics listed above, combining theory with hands-on application in Stata. Each session opens with a Wooclap quiz (5 questions) on the previous session's material, followed by an immediate correction; it is punctuated by short practical Stata exercises and conceptual comprehension questions.

The course is complemented by practical sessions (TP), organized in groups, during which students apply (under the supervision of a teaching assistant) the concepts covered in class to empirical applications in Stata.

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course

Further information:

Course delivered exclusively face-to-face (2h sessions) in the first term
Practical sessions (2h) combining a hands-on Stata exercise and a theoretical exercise.

Each student must bring their own computer or, failing that, use a computer in the university's computer room.

Course materials and recommended or required readings

Platform(s) used for course materials:
- LOL@

Further information:

Course materials available on LOL@: lecture slides, exercise sheets and answer keys for the TPs.

Key references:

Wooldridge, J. M. (2023). Introduction à l'économétrie : une approche moderne (3rd ed.). De Boeck Supérieur.
Stock, J. H. & Watson, M. W. (2020). Introduction to Econometrics (4th ed.). Pearson.
Oswald, F., Viers, V., Robin, J.-M., Villedieu, P., & Kenedi, G. (2020). Introduction to Econometrics with R. Sciences Po, Department of Economics. Online textbook.
Angrist, J. D., & Pischke, J.-S. (2015). Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press.

Exam(s) in session

Any session

- In-person

written exam ( multiple-choice questionnaire, open-ended questions )

Written work / report

Further information:

Additional information:

- Data analysis project (in groups of 4) = 1 bonus point (out of 20)

January session:

Dataset and instructions provided in early November
Submission deadline: mid-January on Lola

August session:

Carry-over of the bonus point earned (or not) during the term (January session)

- Exam(s) in session, in two parts:

Practical MCQ part on computer
Theoretical part (open-ended questions) testing knowledge and understanding of the course.

Econometrics