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IESEG School of Management ( IÉSEG )

Code Cours :



Niveau Année de formation Période Langue d'enseignement 
Post-Graduate Program1S1English
Professeur(s) responsable(s)J.LEFEBVRE

Pré requis

The following concepts and basic knowledge should be managed: random variables; probability notions; basic probability distributions such as Gaussian/normal, Student t, Chi-square and Fisher distributions; statistics such as expectation/mean, variance/standard deviation, correlation, skewness and kurtosis; hypothesis testing using specific probability distributions, p-values (i.e. critical thresholds for tests) and confidence intervals.

Objectifs du cours

At the end of the course, the student should be able to :
- Identify relevant explanatory factors to consider in the regression;
- Check for linearity in existing relationships, or transform data to apply robustly a linear regression;
- Estimate the regression model in a robust manner;
- Interpret the regression’s output in both a statistical and an economic/financial viewpoint;
- Handle the properties of the studied economic/financial/accounting series so as to adjust or make robust data forecasts when possible (e.g. breaks in time series).

Contenu du cours

Based on real-life examples and in course applications, the lectures will cover the following topics:
- Correlation tests to check for linearity between variables;
- Simple linear regression;
- Multiple linear regression;
- Graphical investigation of breaks in time series and usage of dummy variables;
- Validation of regressions (i.e. fulfillment of key assumptions, residuals’ autocorrelation and heteroskedasticity), and some regression issues (e.g. time-varying variance of residuals);
- Interest, use and limitations of linear regressions.

Modalités d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Face to face
Interactive class16,00   In class applications with Eviews and Excel
Independent study
Estimated personal workload6,00  
Group Project10,00  
Charge de travail globale de l'étudiant32,00  

Méthodes pédagogiques

  • Project work
  • Interactive class
  • Case study
  • Coaching
  • Tutorial


1) Financial econometrics project (each group of students works on a selected dataset).
2) A final exam composed of problems, well-chosen course questions (students can bring a two-sided A4 cheat sheet only devoted to valuation formulas), and in class applications on Eviews to study a given dataset.

Type de ContrôleDuréeNombrePondération
Final Exam
Written exam2,00140,00
Group Project0,00560,00
TOTAL     100,00


  • DeFusco, R. A., McLeavey, D. W., Pinto, J. E., & Runkle, D. E. (2011). Quantitative Investment Analysis (2nd ed.). John Wiley & Sons. -


Ressources internet

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