Universität Wien

040121 UK Applied Econometrics 1 (2022S)

4.00 ECTS (2.00 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. 120 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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!

Lecture:
Tuesdays (07.03.22-25.04.22) 13:15-14:45; see class information
Thursdays (03.03.22-28.04.22) 13:15-14:45; see class information

Tutorial:
Mondays (07.03.22-25.04.22) 09.45-11.25, 13.15-14.45; see class information
Wednesdays (09.03.22-27.04.22) 08.00-09.30; see class information

Online Tutorial:
Thursdays (03.03.22-28.04.22) 09:00-10:30; Digital

  • Tuesday 01.03. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 03.03. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 07.03. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Monday 07.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 08.03. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 09.03. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 10.03. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 14.03. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Monday 14.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 15.03. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 16.03. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 17.03. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 21.03. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Monday 21.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 22.03. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 23.03. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 24.03. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 28.03. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Monday 28.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 29.03. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 30.03. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 31.03. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 04.04. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Monday 04.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 05.04. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 06.04. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 07.04. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 25.04. 09:45 - 11:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Monday 25.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 26.04. 13:15 - 14:45 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 27.04. 08:00 - 09:30 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 28.04. 13:15 - 14:45 Digital

Information

Aims, contents and method of the course

The aim of the course is to provide students a thorough understanding of theoretical foundations and proper applications of basic econometric techniques of least squares estimation and time series analysis. Starting from linear regression and generalizations thereof the course covers univariate and multivariate time series analysis. Topics will include basic concepts of stochastic processes, stationarity and ergodicity, estimation and testing of ARMA models, unit root testing, vector autoregressive processes as well as Granger causality. Examples and applications will be illustrated using the open-source software R.
In an accompanying tutorial, students will deepen the material based on exercises and applications using R.

The course consists of regularly two classes per week (unless there are holidays), taught by Nikolaus Hautsch. Moreover, Luca Gonzato will offer three tutorial groups per week. The tutorials will cover exercises, will deepen the material from the classes and will prepare you for the examinations. It is recommended to regularly attend one of the three groups. Finally, student assistant Luzi Watzinger will offer an R tutorial once per week. This tutorial is an additional and accompanying service and intended for students who have insufficent background in R and require more support and practical exercises.
If permitted by Covid regulations, the classes and tutorials will be taught in presence. Otherwise, the sessions will be done remotely via Zoom. The R tutorials will be taught exclusively digitally via Zoom. All necessary information and possible short-term announcements will be provided through the Moodle site of the course.

Assessment and permitted materials

The assessment consists of the following parts:

i) Exam, 45 min, 28.4., 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 via Moodle.

ii) Take-home assignments. Students have to solve and have to hand in written assignments. They can consist of multiple-choice questions, analytical derivations and interpretations of empirical results.

(iii) Empirical take-home project, 2.5.-15.5. 2022. During the first week (2.5.-8.5.) 50% of the students (randomly chosen) 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. The effective working time corresponds approximately to one working day, but students have one week to perform the analysis. Students have to work remotely in groups. The number of students per group will depend on the number of course participants. The allocation will be done randomly or via self-coordination (will be announced in due time).
During the second week (9.5.-15.5) the remaining 50% of the students will be allocated to similar groups, where each group will be (randomly) assigned to one of the uploaded papers. The task is then to perform an own analysis and to critically evaluate the initial analysis. Students have to write a review, where they assess the initial study and come up with suggestions for improvements.
Download of data and instructions as well as upload of reports and R codes are performed through Moodle.

Minimum requirements and assessment criteria

For the final grade the individual assignments count as follows:
i) Exam: 40%
ii) Assignments: 20%
iii) Take-home project: 40%

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.

[63%; 75%): 3.0
[50%; 63%): 4.0
[0; 50%): 5.0

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

1. Das Lineare Regressionsmodell
2. Erweiterungen und Anwendungen des linearen Regressionsmodells
3. Zeitreihenanalyse

Reading list

Dougherty, C., “Introduction to Econometrics”, 3rd ed., Oxford University Press, 2007.
Franses, P. H., van Dijk, D., and Opschoor, A., “Time Series Models for Business and Economic Forecasting”, 2nd ed., Cambridge University Press, 2014.
Heij, De Boer, Franses, Kloek, and Van Dijk, ''Econometric Methods with Applications in Business and Economics'', Oxford University Press, 2004.
Stock, J.H., Watson, M.W., ''Introduction to Econometrics'', 3rd edition, Pearson, 2012.
Studenmund, A. H. “Using Econometrics”, 6th ed., Pearson, 2011

Online Literatur basierend auf R:

Association in the course directory

Last modified: Th 11.05.2023 11:27