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  • Teacher(s):   J.Renne  
  • Course given in: English
  • ECTS Credits: 6 credits
  • Schedule: Spring Semester 2021-2022, 4.0h. course (weekly average)
  •  sessions
  • site web du cours course website
  • Related programme: Master of Science (MSc) in Economics
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This course proposes an introduction to time series analysis. Time series constitute a prevalent data type that arises in several disciplines, notably in macroeconomics and finance. The modelling of time series is crucial for many purposes, including forecasting, the understanding of macroeconomic mechanisms and risk assessment.

The theoretical presentation of standard and more advanced modelling tools will be complemented with laboratory sessions. The latter will in particular bring students to implement time series tools on real data.


The notions covered by the course are the following:

A- Modelling of univariate processes:

  • ARMA processes: presentation, estimation and forecasting.
  • Stochastic-volatility models (ARCH and GARCH models): presentation, estimation and forecasting.

B- Modelling of multivariate processes:

  • Vector autoregressive models (VAR).
  • Derivation of impulse-response functions, which are key tools in macroeconomic analysis. The issue of the structural-shock identification will notably be explored.
  • Cointegration issues.

C- If time allows, regime-switching models will be studied.

Laboratory sessions will illustrate the preceding items. Students will learn and use the R software.


* Gourieroux, C., and A. Monfort (1995), Statistics and Econometrics Models, volumes 1 and 2, Cambridge University Press.

* Hamilton, J. (1994), Time Series Analysis, Princeton University Press.

* Stock, J., and Watson, M. (2003), Introduction to Econometrics, Addison-Wesley Series in Economics.


Introductory econometrics and statistics.


First attempt

Written 1h15 hours
Allowed with restrictions

The final grade is an equally-weighted average of two grades:

  1. The first grade is based on an onsite digital (ENEP) exam (1h15).
  2. The second grade is based on a project making use of real data. This project is carried out by groups of up to three individuals. Students will be encouraged to begin their project relatively early in the semester (mid-term) to benefit from laboratory sessions that will be dedicated to the projects. The groups will present their results during the last sessions. A report (pdf file) as well as the codes and data file will be sent to the professor the week before the presentation.


Written 1h15 hours
Allowed with restrictions

In the case of re-take, the students have to work on their project and to present it again. They will also have a 1h15-long onsite digital (ENEP) exam. The final grade is an equally-weighted average of the two grades (project and written exam).

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