FIN-403 / 4 crédits

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.

## Keywords

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

## 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 or Python is recommanded for practicals and the graded project (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..
• Conduct team-work and write an econometric report about linear and nonlinear regression models.

## Transversal skills

• Use a work methodology appropriate to the task.
• Continue to work through difficulties or initial failure to find optimal solutions.
• Write a scientific or technical report.
• Use both general and domain specific IT resources and tools

## Teaching methods

Lectures and exercise sessions.

## Expected student activities

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

## Assessment methods

• 25% Project in group
• 75% Final written exam

## Supervision

 Office hours No Assistants Yes Forum No

No

## 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.
• Wooldridge, J. M. (2018) Introductory Econometrics: A Modern Approach. Seventh edition. Boston: Cengage.

## 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

 Lu Ma Me Je Ve 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22