Universität Wien

040111 KU Introductory Econometrics (MA) (2021W)

8.00 ECTS (4.00 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. 100 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 05.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 07.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 12.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 14.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 19.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 21.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 28.10. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 04.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 09.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 11.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 16.11. 16:45 - 18:15 Digital
  • Thursday 18.11. 16:45 - 18:15 Digital
  • Tuesday 23.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 25.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 30.11. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 02.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 07.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 09.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 14.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 16.12. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 11.01. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 13.01. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 18.01. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Thursday 20.01. 16:45 - 18:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Tuesday 25.01. 16:45 - 18:15 Digital
  • Thursday 27.01. 16:45 - 18:15 Digital

Information

Aims, contents and method of the course

Aims and Contents
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 tutorials accompanying the course.

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.

Form of Teaching:
The course will be taught in a “hybrid” way. Lectures take place in the lecture hall and students can enter according to current Covid regulations. All students who cannot enter the lecture hall (due to covid regulations) can follow the course at the same time remotely via Zoom. Corresponding links will be provided on the Moodle site of the course.
If necessary due to Covid regulations, the lecture will be held in an entirely digital form via Zoom. Corresponding announcements will be done via Moodle.

Assessment and permitted materials

The assessment consists of the following parts:

i) 2 small tests, 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. In case of hybrid teaching, students, who cannot be present in class, have to do an equivalent test at the same time remotely via Moodle.

ii) Written exam, 60 min, 27.1. 2022, 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 (completely or partly) remotely via Moodle.

iii) Empirical take-home project, 21.2.-27.2. 2022. Students will receive datasets and have to perform econometric analyses in R in order to address certain economic questions. The analysis and results have to be documented in a research report (max. 7 pages), and R codes used in the study have to be uploaded. All results must be easily replicable in R. Students have to work remotely in groups of 3 or 4 persons (will be announced in due time depending on the number of students in the course). The effective working time corresponds approximately to one working day, but students have one week to perform the analysis. Download of data and instructions as well as upload of reports and R codes are performed through Moodle.

Permitted material: No additional material except a calculator.

Minimum requirements and assessment criteria

All material covered in class and in the tutorials.

Examination topics

Grading:
For the final grade the individual assignments count as follows:
i) 2 Tests: 25% each
ii) Exam: 50%
iii) Take-home project: 50%
Out of these three assignments, only the two best ones count for the final grade.

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

Reading list

Literature

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: Fr 12.05.2023 00:12