Universität Wien
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040012 UE Econometric Programming in Economics (MA) (2023S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
MIXED
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

  • Monday 06.03. 11:30 - 13:00 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Friday 10.03. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 17.03. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Monday 20.03. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 24.03. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 31.03. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Monday 17.04. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 21.04. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Monday 24.04. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 28.04. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 05.05. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Friday 12.05. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Monday 15.05. 09:45 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

Aims, contents and method of the course

NOTE: The course will be offered in hybrid format (in-class and via live stream) if the class size exceeds the maximum capacity of the computer labs.

Content:

This course provides students with the skill set to conduct econometric analysis. Building upon the introductory course of econometrics, students will get a deeper understanding of the relevant methods used in contemporary empirical analysis and they will learn how to implement these methods using the statistical software R. While the techniques and methods are also introduced and discussed in class, the emphasis of the course is on their application in R.Typical questions of this course are, among others: What methods are available to estimate coefficients and standard errors of a specific regression model? What are the implications of missing data or measurement error? Which techniques are suitable for the analysis of duration data? How can (local) average treatment effects be estimated? What methods are available to exploit quasi-experimental variation?

Course goals:

At the end of the course, students will be familiar with the relevant techniques and tools used for empirical analysis in applied economics. This will allow them to critically read, understand and replicate empirical research. For a given research question and dataset, students will be able to choose appropriate econometric methods and conduct empirical analysis using R.

Prerequisites:

- Knowledge of statistics and applied econometrics (MA course “Introductory Econometrics” or equivalent)
- Basic knowledge of R or similar statistical software

Assessment and permitted materials

The course assessment consists of a take-home assignment (45%), an empirical research project (45%) and participation in class (10%).

Minimum requirements and assessment criteria

Students are encouraged to regularly attend the course sessions. The final grade will be a weighted average of grade points obtained for the three assessment parts that are listed above.

Examination topics

Econometric methods and statistical programming with R (see course content).

Reading list

Methods:

- Joshua D. Angrist and Jörn-Steffen Pischke. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press, 2008.
- Colin A. Cameron and Pravin K. Trivedi. Microeconometrics: Methods and Applications. Cambridge University Press, 2005.

Statistical programming:

- Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer. Introduction to Econometrics with R, 2021. (E-book)
- Florian Heiss. Using R for Introductory Econometrics. Second edition, 2020. (E-book)

Association in the course directory

Last modified: We 01.03.2023 09:48