Universität Wien

040033 KU Econometrics II (MA) (2022S)

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

Lecture:
Tuesdays (01.03.2022-28.06.2022) 15:00-16:30; See Course for more information
Thursdays (03.03.2022-30.06.2022) 15:00-16:30; See Course for more information

Tutorial:
Thursdays (03.03.2022-28.06.2022) 13:15-14:45; See Course for more information

  • Tuesday 01.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 03.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 03.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 08.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 10.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 10.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 15.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 17.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 17.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 22.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 24.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 24.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 29.03. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 31.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 31.03. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 05.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 07.04. 08:00 - 09:30 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 07.04. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 26.04. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 28.04. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 28.04. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 03.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 05.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 05.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 10.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 12.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 12.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 17.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 19.05. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 19.05. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 24.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 31.05. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 09.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 09.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 14.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 21.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 23.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 23.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 28.06. 15:00 - 16:30 Hörsaal 5 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 30.06. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Thursday 30.06. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Friday 01.07. 09:45 - 16:30 Seminarraum 8 Oskar-Morgenstern-Platz 1 2.Stock
    Seminarraum 9 Oskar-Morgenstern-Platz 1 2.Stock
    Seminarraum 9 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 18.07. 09:45 - 16:30 Seminarraum 8, Kolingasse 14-16, OG01
    Seminarraum 9, Kolingasse 14-16, OG01

Information

Aims, contents and method of the course

This course provides students a deeper understanding of theory and practice of major parametric estimation and testing techniques in econometrics. The course will cover the class of extremum estimators and their asymptotic properties, with a special focus on (pseudo) maximum likelihood, nonlinear least squares as well as generalized methods of moments (GMM) estimation. Moreover, students will learn basic principles of bootstrap methods as well as simulation-based methods and (Bayesian) filtering techniques.
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 until March 15, 2022. Students who are still signed in after March 15, 2022 will be graded!

Assessment and permitted materials

The assessment consists of the following parts:

i) One small test, ca. 30 min, without or with short advance notice during the semester. 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, part of the exams might be done in oral form during the first 2 weeks after the end of the term. The written part of the exam will take place during the regular lecture on 23.6. 2022.

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: 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: Students can do the examinations in English or German, but have to stick to one language.

Examination topics

All topics covered in the course

Reading list

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.

Association in the course directory

Last modified: Tu 14.06.2022 16:47