Universität Wien
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040033 KU Econometrics II (MA) (2024S)

10.00 ECTS (5.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 05.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 07.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 07.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 14.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 14.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 19.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 21.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 21.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 09.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 11.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 11.04. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 16.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 18.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 18.04. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 23.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 25.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 25.04. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 30.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 02.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 02.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 07.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 14.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 16.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 16.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 21.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 23.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 23.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 28.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 04.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 06.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 06.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 11.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 13.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 13.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 18.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 20.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 20.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 25.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 27.06. 13:15 - 14:45 Hörsaal 15 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 27.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 02.07. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

Aims, contents and method of the course

This course provides students with a deeper understanding of the theory and practice of major parametric estimation and testing techniques in econometrics. The course will cover asymptotic inference, non-linear least squares, maximum and quasi-maximum likelihood estimation, likelihood-based testing, as well as generalized methods of moment estimation. If time allows, selected topics such as indirect inference, simulated maximum likelihood, and advanced time series methods will be discussed.
After following this course, students will have a good working knowledge of statistical inference as applied in various areas of modern econometrics, including time series econometrics, micro econometrics, and financial econometrics. In the tutorials, students will deepen the material based on exercises, examples and applications using the open-source software R.

Prerequisites
Students need to have basic econometric knowledge as taught in the course “Introductory Econometrics” or a similar course. Moreover, basic knowledge in R is required.

Signing-off
Signing off is only possible until at latest March 21, 2024. Students who are still signed in after March 21, 2024 will be graded!

Assessment and permitted materials

The assessment consists of the following parts:

i) One small test, ca. 60 min, during the semester. It can consist of multiple-choice questions, analytical derivations and interpretations of empirical results.

ii) Exam, 60 min, on all topics covered in the course. It can consist of multiple-choice questions, analytical derivations and interpretations of empirical results. Depending on the number of course participants, the exams might be done in oral form

iii) Take-home assignments. Students have to solve and have to hand in weekly or bi-weekly written assignments. They can consist of multiple-choice questions, analytical derivations and interpretations of empirical results. The solutions may also have to be presented in the tutorials.

Permitted material: No additional material except a calculator.

Minimum requirements and assessment criteria

Grading:
For the final grade the individual assignments count as follows:
i) Test: 25%
ii) Exam: 40%
iii) Assignments: 35%

Important: Aside from the three assignments, there will be no additional examination possibilities afterwards.

To pass the course, a minimum level of 45% has to be reached.

Rating:
[85%; 100%]: 1.0
[70%; 85%): 2.0
[55%;70%): 3.0
[45%; 55%): 4.0
[0; 45%): 5.0

Examination language: English

Examination topics

All topics covered in the course

Reading list

Davidson, R. and J. MacKinnon (2004): Econometric Theory and Methods, Oxford University Press

Gouriéroux, C. and A. Monfort (1995): Statistics and Econometric Models, Cambridge University Press.

Gouriéroux, C. and A. Monfort (1996): Simulation-Based Econometric Methods, Oxford University Press.

Hansen, Bruce E. (2019): Econometrics. Freely available at: http://www.ssc.wisc.edu/~bhansen/econometrics/

Hayashi, F. (2000): Econometrics, Princeton University Press.

Newey, W. K. (1993). “Efficient Estimation of Models with Conditional Moment Restrictions, “ Handbook of Statistics, 11, 419-453.

Newey, W. K. and D. McFadden (1994): “Large Sample Estimation and Hypothesis Testing”, in Handbook of Economtrics, e.d by R. F. Engle and D. L. McFadden, Elsevier, Chap. 36, 2111-2245.

Association in the course directory

Last modified: We 31.07.2024 11:25