Universität Wien

040033 KU Econometrics II (MA) (2024S)

10.00 ECTS (5.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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!

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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

Prüfungsstoff

All topics covered in the course

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 31.07.2024 11:25