# Fiches de cours

## Econometrics

English

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

- Linear models;
- Least squares regression;
- Instrumental variables;
- Nonlinear models;
- Nonspherical errors;
- Large sample asymptotics: consistency, efficiency and limit distribution of estimators;
- Hypothesis testing.

#### Keywords

Linear regression models; least squares estimation; maximum likelihood inference.

#### Learning Prerequisites

##### Recommended courses

• Analysis;
• Linear algebra;
• Introduction to probability and statistics;
• Introduction to economics.

##### Important concepts to start the course

• Matrix algebra;
• Probability and distribution theory;
• Large-sample distribution theory;
• Familiarity with R and/or Matlab is recommanded for simulations and empirical analyses.

#### 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

Ex cathedra lectures and exercise sessions.

#### Expected student activities

Attending lectures, reading written material, completing exercises and group homework.

#### Assessment methods

• 20% Homework assignments;
• 30% Midterm examination (written, closed book);
• 50% Final examination (written, closed book).

#### Supervision

 Office hours No Assistants Yes Forum No

#### Resources

##### Bibliography

Bierens, H. J. (2004). Introduction to the mathematical and statistical foundations of econometrics. New York: Cambridge University Press.
Greene, W. H. (2012) Econometric analysis. Seventh edition. Edinburgh: Pearson Education Limited.
Hayashi, F. (2000) Econometrics. Princeton: Princeton University Press.
Stock, J. H. and Watson, M. W. (2015) Introduction to Econometrics. Third edition. Edinburgh: Pearson Education Limited.

Lecture notes.

#### Prerequisite for

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

### Semaine de référence

LuMaMeJeVe
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
En construction

Cours
Exercice, TP
Projet, autre

### légende

• Semestre d'automne
• Session d'hiver
• Semestre de printemps
• Session d'été
• Cours en français
• Cours en anglais
• Cours en allemand