Universität Wien
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040111 KU Introductory Econometrics (MA) (2022W)

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
ON-SITE
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. 200 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

UK, 4 hours per week (8 ECTS)

accompanied by
- a weekly tutorial (Übung: 040115 UE Introductory Econometrics; MA- 2022W; https://ufind.univie.ac.at/de/course.html?lv=040115&semester=2022W), 1 hour per week (2 ECTS)
- a weekly or bi-weekly Zoom-based R tutorial (voluntarily, but highly recommended)

Sign-In
Students have to sign in during the first week of the semester. Signing off is only possible until at latest until October 15, 2022. Students who are still signed in after October 15, 2022 will be graded!

  • Tuesday 04.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 06.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 11.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 13.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 18.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 20.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 25.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 27.10. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 03.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 08.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 10.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 15.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 17.11. 16:45 - 18:15 Digital
  • Tuesday 22.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 24.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 29.11. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 01.12. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 06.12. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 13.12. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 15.12. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 10.01. 18:00 - 19:30 Digital
  • Thursday 12.01. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 19.01. 16:45 - 18:15 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 24.01. 18:00 - 19:30 Digital
  • Thursday 26.01. 18:00 - 19:30 Digital
  • Tuesday 31.01. 16:45 - 17:45 Digital

Information

Aims, contents and method of the course

Aims and Contents
The course is a first-year master-level course in econometrics for students who already have a background in statistics and are familiar with basic principles of probability theory, mathematical statistics and basic principles of linear regression. The course provides an understanding of basic econometric methods. Knowledge of these methods allows one to understand modern empirical economic literature and to perform one's own analysis of cross-sectional, time series, and panel data. After following this course, students will have a good working knowledge of the key properties of standard econometric methods, including Least Squares Estimation, Instrumental Variables Estimation, and Maximum Likelihood, and their use in various applications.

The course emphasizes the application of econometric techniques as well as the
interpretation of models and outcomes of estimation and testing procedures. The students practice this by analyzing economic data by means of the open-source software R. They also learn to implement basic matrix-based formulas and to interpret theoretical results by simulating data generating processes and estimators based on small programming tasks in R. Econometric applications based on R will be illustrated in class and trained in a tutorial accompanying the course. For those who are not familiar with the software R, it is highly recommended to follow the online R-tutorial. It will consist of weekly uploads of pre-recorded videos, many resources as well as a R-specific moodle forum for all of your R-related questions.

Topics include foundations of least squares estimation, applications of linear regression, endogeneity and instrumental variable estimation, stationary ARMA models, non-stationary time series models, fixed effects and random effects estimation, logistic regression, regression with limited dependent variables, among others.
If not compulsory, it is highly recommended to also attend the weekly tutorial (Uebung: https://ufind.univie.ac.at/de/course.html?lv=040115&semester=2022W), which takes place in parallel to the lecture. For those who are not familiar with the software R, it is highly recommended to also attend the weekly online tutorial.

Form of Teaching
If permitted by Covid regulation, the course will be taught in physical presence in class room. Whenever necessary due to Covid restrictions, a hybrid or fully digital format via Zoom will be chosen. Corresponding announcements will be done via Moodle.

Assessment and permitted materials

Assessment
The assessment consists of 3 tests during the semester (approximately after every 4th to 5th week). The Tests will take place on following sessions:
3.11.2022
15.12.2022
31.1.2023
The tests will take approximately 45-60 minutes, will be carried out remotely through Moodle and will be in a multiple-choice format. The questions will refer to general material covered in the course, analytical derivations, interpretations of empirical results as well as (small) empirical analysis , students have to carry out “on the fly” in R.

Each test will count 50 %. Out of these three assignments, only the two best ones count for the final grade.

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

Minimum requirements and assessment criteria

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

Examination Topics
All material covered in the course.

Reading list

Angrist, J.D. and Pischke, J.-S. (2009): Mostly Harmless Econometrics: An Empiricst's Companion, Princeton University Press.

Brockwell, P.J., and Davis, R.A. (2002): Introduction to Time Series and Forecasting, 2nd edition, Springer.

Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M. (2020): Introduction to Econometrics with R, Online book on : https://www.econometrics-with-r.org/. Based on Stock, J. H., and Watson, M. W. (2015), Introduction to Econometrics, Global Edition. Pearson Education Limited.

Heiss, F. (2020): “Using R for Econometrics”. Online book on http://www.urfie.net/. Based on Wooldridge, J.M. (2019), Introductory Econometrics, Cengage Learning, Boston, MA.

Association in the course directory

Last modified: Th 11.05.2023 11:27