FIN-403 / 4 crédits

Enseignant: Fuster Andreas

Langue: Anglais

Remark: For sem. MA1


Summary

The course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications.

Content

Keywords

Econometrics; linear regression; ordinary least squares; instrumental variables; generalized method of moments; maximum likelihood estimation.

Learning Prerequisites

Recommended courses

Important concepts to start the course

  • Matrix algebra;
  • Probability and distribution theory (incl. conditional expectation and variance, normal, Chi-squared, Student, and F distributions);
  • Statistical estimation and inference (incl. point estimation, interval estimation, hypothesis testing);
  • Large-sample distribution theory (incl. convergence in probability, convergence in distribution, central limit theorem, Delta method);
  • Familiarity with R, Matlab, Python or Stata is recommended for applied exercises (e.g., empirical analysis, simulations).

Learning Outcomes

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

  • Describe the basic assumptions of the linear regression model.
  • Test whether the basic assumptions of the linear regression model are met in the data using formal statistical procedures.
  • Derive statistical estimators like least squares and instrumental variables estimators.
  • Recall basic goodness-of-fit measures like R-squared.
  • Construct linear regression models from actual data using statistical variable-selection techniques like t-statistics and F-tests.
  • Describe the main advantages and disadvantages of likelihood-based and instrumental variable-based inference procedures.
  • Carry out linear and nonlinear hypothesis testing procedures.
  • Discuss asymptotic properties of linear and nonlinear estimators such as consistency and efficiency.
  • Apply the theoretical concepts using econometric software to analyze actual data.

Transversal skills

  • Use a work methodology appropriate to the task.
  • Continue to work through difficulties or initial failure to find optimal solutions.
  • Use both general and domain specific IT resources and tools
  • Demonstrate the capacity for critical thinking

Teaching methods

Lectures and exercise sessions.

Expected student activities

  • Attend and participate in lectures;
  • Attend and participate in exercise sessions;
  • Review lecture material and complete exercises,
  • Write a midterm exam;
  • Write a final exam.

Assessment methods

  • 15% Applied problem sets
  • 25% Midterm exam
  • 60% Final written exam

Supervision

Assistants Yes

Resources

Virtual desktop infrastructure (VDI)

Yes

Bibliography

  • Brooks, C. (2019) Introductory Econometrics for Finance. Fourth edition. Cambridge: Cambridge University Press.
  • Davidson, R., Mackinnon, J. G. (2009) Econometric Theory and Methods. International edition. Oxford: Oxford University Press.
  • Greene, W. H. (2018) Econometric analysis. Eighth edition. New York: Pearson Education Limited.
  • Hayashi, F. (2000) Econometrics. Princeton: Princeton University Press.
  • Verbeek, M. (2017) A Guide to Modern Econometrics. Fifth Edition. Hoboken: John Wiley & Sons.
  • Wooldridge, J. M. (2018) Introductory Econometrics: A Modern Approach. Seventh edition. Boston: Cengage.

Ressources en bibliothèque

Prerequisite for

  • Advanced topics in financial econometrics
  • Credit risk
  • Derivatives
  • Financial econometrics
  • Fixed income analysis
  • Investments

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Econometrics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Econometrics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Ecrit (session d'hiver)
  • Matière examinée: Econometrics
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16   BS150 
16-17 BS270  
17-18    
18-19     
19-20     
20-21     
21-22     

Mardi, 16h - 18h: Cours BS270

Jeudi, 15h - 17h: Exercice, TP BS150