Universität Wien

040033 KU Econometrics II (MA) (2023S)

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

  • Donnerstag 02.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 02.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 07.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 09.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 09.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 14.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 16.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 16.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 21.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 23.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 23.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 28.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 30.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 30.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 18.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 20.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 20.04. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 25.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 27.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 27.04. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 02.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 04.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 04.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 09.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 11.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 11.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 16.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Dienstag 23.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 25.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 25.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 01.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 01.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 06.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Montag 12.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Donnerstag 15.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 15.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 20.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 22.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 22.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Dienstag 27.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Donnerstag 29.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Donnerstag 29.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course provides students a deeper understanding of theory and practice of major parametric estimation and testing techniques in econometrics. The course will cover maximum likelihood, nonlinear least squares as well as generalized methods of moments (GMM) estimation. If time allows, topics from indirect inference and advanced time series econometrics will be considered.
After following this course, students will have a good working knowledge of statistical inference as applied in various areas in modern econometrics, including time series econometrics, micro econometrics, panel econometrics as well as financial econometrics. In the tutorials, students will deepen the material based on exercises, examples and applications using the open-source software R.

The course will be taught in class. If required due to Covid regulations, the course will be done remotely via Zoom. All necessary information and possible short-term announcements will be provided through the Moodle site of the course.

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.

Sign-In
Students have to sign in during the first week of the semester. Signing off is only possible until at latest March 15, 2023. Students who are still signed in after March 15, 2023 will be graded!

Art der Leistungskontrolle und erlaubte Hilfsmittel

The assessment consists of the following parts:

i) One midterm test, ca. 60 min. Can consist of multiple-choice questions, analytical derivations and interpretations of empirical results. Depending on the pandemic situation, the tests might be done via Moodle.

ii) Exam, 60 min, on all topics covered in the course. Can consist of multiple-choice questions, analytical derivations and interpretations of empirical results. Depending on the pandemic situation, the exam might be carried out via Moodle. 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: 20%
ii) Exam: 45%
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

Adda, J. and R. W. Cooper (2003): Dynamic Economics – Quantitative Methods and Applications. MIT Press.

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.

Greenberg, E. (2008): Introduction to Bayesian Econometrics, Cambridge 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 20.12.2023 12:05