FIN-403 / 4 credits

Teacher: Fuster Andreas

Language: English

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

- Linear regression models
- Ordinary least squares estimation
- Hypothesis testing and confidence intervals in linear regression models
- Heteroskedasticity and autocorrelation
- 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 estimation.

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);
• 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 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.
• Apply the theoretical concepts using econometric software to analyze actual data.
• Discuss asymptotic properties of linear and nonlinear estimators such as consistency and efficiency.

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/projects,
• Write a midterm exam;
• Write a final exam.

Assessment methods

• 15% Applied projects
• 25% Midterm exam
• 60% Final written exam

Supervision

 Office hours No Assistants Yes Forum Yes

Yes

Bibliography

• Davidson, R., Mackinnon, J. G. (2009) Econometric Theory and Methods. International edition. Oxford University Press.
• Greene, W. H. (2018) Econometric analysis. Eighth edition. Pearson.
• Hayashi, F. (2000) Econometrics. Princeton: Princeton University Press.
• Stock, J., Watson, M. (2019) Introduction to Econometrics. Fourth Edition. Pearson.
• 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.

Prerequisite for

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

In the programs

• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Econometrics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Type: mandatory
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Econometrics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Type: mandatory
• Semester: Fall
• Exam form: Written (winter session)
• Subject examined: Econometrics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Type: optional

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