Program Overview

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Introduction to Econometrics

The course provides an introduction to econometrics for economics and financial applications. The objective is to learn how to make valid (i.e., causal) inference from economic and social data.

Content

• Causal inference
• Linear model
• Estimation (ordinary least square, maximum likelihood)
• Inference (hypothesis testing, confidence intervals)
• Panel data
• Experiments and quasi-experiments
• Instrumental variable
• Introduction to time series

Keywords

Econometrics; Statistics; Data analysis; Causality; Data science; Ordinary least squares; Linear model

Learning Prerequisites

• Sound understanding of statistics and probability concepts (central limit theorem, hypothesis testing, etc.).
• Matrix algebra.
• Familiarity with R (or Python) is helpful.

Learning Outcomes

By the end of the course, the student must be able to:

• Recognize pitfalls and bias in data collection and econometric models
• Illustrate the concept of endogeneity
• Check the validity of an econometric result
• Quantify an economic relationship
• Design an appropriate regression model
• Interpret coefficients of econometric regressions
• Carry out hypothesis testing

Transversal Skills

• Demonstrate a capacity for creativity.
• Demonstrate the capacity for critical thinking
• Use both general and domain-specific IT resources and tools
• Use a work methodology appropriate to the task.
• Access and evaluate appropriate sources of information.

Teaching Methods

Lectures provide the theoretical knowledge and exercise sessions illustrate theory using computer exercises.

Expected Student Activities

• Attendance and participation at lectures and exercise sessions
• Submission of problem sets

Assessment Methods

• Individual problem sets/assignments: 40%
• Written exam during the exam session: 60%

Resources

Bibliography

The course will be based on:

• Morgan, Steven L., and Christopher Winship. 2014. Counterfactuals and Causal Inference: Methods and Principles for Social Research. 2nd Edition. Cambridge University Press
• James H. Stock and Mark W. Watson. Introduction to Econometrics. 3rd Edition. Pearson.
• Verbeek, M. 2017. A Guide to Modern Econometrics. 5th Edition. John Wiley & Sons.

Additional useful references:

• Angrist, J.D. and Pischke, J.-S. 2009. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.
• Aronow, Peter M., and Benjamin T. Miller. 2019. Foundations of Agnostic Statistics. Cambridge University Press.
• Gelman, Andrew, and Jennifer Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
• Greene, W.H. 2011. Econometric Analysis. Prentice Hall.
• Hansen, B. 2022. Princeton University Press
• Pearl, J. 2009. Causality (2nd ed). Cambridge University Press.
• Stachurski, J. 2016. A Primer in Econometric Theory. MIT Press.

Programs

The course is part of the following programs:

• Management, Technology and Entrepreneurship Master semester 1
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Introduction to econometrics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory
• Management, Technology and Entrepreneurship Master semester 3
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Introduction to econometrics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory
• Financial engineering Master semester 1
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Introduction to econometrics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory
• Financial engineering Master semester 3
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Introduction to econometrics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: mandatory
• Financial engineering minor Autumn semester
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Introduction to econometrics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: optional
• Management of technology Doctoral School
  • Exam form: Written (winter session)
  • Subject examined: Introduction to econometrics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: optional
• Management, Technology and Entrepreneurship minor Autumn semester
  • Semester: Fall
  • Exam form: Written (winter session)
  • Subject examined: Introduction to econometrics
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: optional

Reference Week

| Mo| Tu| We| Th| Fr
---|---|---|---|---|---
8-9| | | | |
9-10| | | | |
10-11| | | | |
11-12| | | | |
12-13| | | | |
13-14| | | | |
14-15| CM1105| | | |
15-16| CM2| | |
16-17| | | |
17-18| | | | |
18-19| | | | |
19-20| | | | |
20-21| | | | |
21-22| | | | |

Légendes: Lecture Exercise, TP Project, Lab, other

Monday, 14h - 16h: Exercise, TP CM1105 Tuesday, 15h - 17h: Lecture CM2

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