# Coursebooks

## 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 regression models
- Ordinary least squares estimation
- Hypothesis testing and confidence intervals in linear regression models
- Nonlinear regression models
- Generalised least squares
- Instrumental variables estimation
- Generalized method of moments
- Maximum likelihood estimation
- Introduction to time series models

#### Keywords

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

#### Learning Prerequisites

##### 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 (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

• Midterm written exam (weight: 35%);
• Final written exam (weight: 65%).

#### Supervision

 Office hours No Assistants Yes Forum No

#### Resources

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

### Reference week

MoTuWeThFr
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
Under construction

Lecture
Exercise, TP
Project, other

### legend

• Autumn semester
• Winter sessions
• Spring semester
• Summer sessions
• Lecture in French
• Lecture in English
• Lecture in German