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  • Enseignant(s):  
  • Titre en français: Econométrie
  • Cours donné en: anglais
  • Crédits ECTS:
  • Horaire: Semestre d'automne 2021-2022, 4.0h. de cours + 1.0h. d'exercices (moyenne hebdomadaire)
  •  séances
  • site web du cours site web du cours
  • Formations concernées:
    Maîtrise universitaire ès Sciences en économie politique

    Maîtrise universitaire en Droit et économie
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This course discusses several of the practically most relevant econometric/statistical methods used in empirical work in economics and management. The course also discusses how to apply these methods in actual data using the statistical software “R (studio)”. The objective is to enable participants (1) to understand differences in the properties and assumptions of the various methods along with their advantages and disadvantages and (2) to apply econometric methods to real-world problems.


The course consists of a lecture and PC lab sessions.

The lecture discusses important econometric methods along with their underlying assumptions and properties. The topics covered include:
1) The difference between causation (e.g. education has a causal effect on wage) and correlation (subjects with higher education have higher wages, but this may be driven by other factors than education as for instance ability); the intuition of experiments for assessing causation.
2) Linear regression (OLS - ordinary least squares) to assess the association of one or several variables (e.g. education, age,...) with an outcome of interest (e.g. wage).
3) Nonlinear regression (probit regression for binary outcomes like working vs. not working, tobit regression for censored outcomes)
4) Instrumental variable regression and regression discontinuity designs under endogeneity
5) Panel data regression and “Differences-in-Differences” estimation when subjects are observed at several points in time.
6) Introduction to time series econometrics, e.g. for modeling stock prices or GDP growth over time.
7) Further topics: Quantile regression to conduct empirical analyses for particular subgroups (e.g. the median earner in the wage distribution), introduction to machine learning (a subfield of Artificial Intelligence)

The PC lab sessions will introduce you to coding and empirical analysis using the statistical software “R (studio)”, one of the most frequently used software packages. You will practically apply the methods of the lecture to real-world data in several problem sets. An introduction to “R (studio)” is provided in the first PC lab.


The material covered in the lecture is primarily based on the following textbook:

  • Jeffrey M. Wooldridge, 2010, "Econometric Analysis of Cross Section and Panel Data", MIT Press, Second Edition.

which can be bought or rented here:

To a lesser extent, it is also based on the discussion in:

  • Angrist, Joshua D. and Jorn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton University Press.


Introductory econometrics and statistics.


1ère tentative

Ecrit 1h30 heures

[ 4 function calculator according to HEC directive ]


Ecrit 1h30 heures

[ 4 function calculator according to HEC directive ]

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