Universität Wien

040122 UK Applied Econometrics 2 (2023S)

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

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. 60 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Lecture:
Tuesdays (02.05.23-27.06.23): 11:30-13:00; see class information
Thursdays (04.05.23-22.06.23): 13:15-14:45; see class information

Tutorial:
Mondays (08.05.23-26.06.23): 08:00-09:30; see class information

Tuesday 02.05. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 04.05. 13:15 - 14:45 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 09.05. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 11.05. 13:15 - 14:45 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Monday 15.05. 09:45 - 11:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 16.05. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 23.05. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 25.05. 13:15 - 14:45 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 01.06. 13:15 - 14:45 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Monday 05.06. 09:45 - 11:15 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 06.06. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Monday 12.06. 09:45 - 11:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Tuesday 13.06. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 15.06. 13:15 - 14:45 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 20.06. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 22.06. 13:15 - 14:45 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Monday 26.06. 09:45 - 11:15 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

Aims, contents and method of the course

Aims and Contents

The aim of the course is to provide students a thorough understanding of theoretical foundations and proper applications of instrumental variable estimation and econometric techniques for panel data and microeconometric data. The course will cover two-stage least squares, seemingly unrelated regression, fixed-effects and random-effects panel estimation as well as econometric models for categorical data and for limited dependent variables. 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.

Form of Teaching
The course will be taught in class. 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) Three Exams, a 45 min:
Part I, 6. June 2023, (HS 14, 11:30-13:00),
Part II, 27. June 2023 (HS 14, 13:15-14:45);
Part III, 27. June 2023 (HS 14, 13.15-14.45);

Questions can consist of multiple-choice questions, analytical derivations and interpretations of empirical results.

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.

Permitted material for (i): No additional material except a calculator.
Permitted material for (ii): open book, we need a laptop for this exam.

Grading:
For the final grade the individual assignments count as follows:
i) Three exams: 75%, 25% each
ii) Assignments: 25%

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

To pass the course, a minimum level of 50% has to be reached.

Rating:
[86%; 100%]: 1.0
[74%; 86%): 2.0
[62%;74%): 3.0
[50%; 62%): 4.0
[0; 50%): 5.0

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

Minimum requirements and assessment criteria

The assessment consists of the following parts:

(i) Three Exams, a 45 min:
(i) Three Exams, a 45 min:
Part I, 6. June 2023, (HS 14, 11:30-13:00),
Part II, 27. June 2023 (HS 14, 13:15-14:45);
Part III, 27. June 2023 (HS 14, 13.15-14.45);
Questions can consist of multiple-choice questions, analytical derivations and interpretations of empirical results.

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.

Permitted material for (i): No additional material except a calculator.
Permitted material for (ii): open book, we need a laptop for this exam.

Grading:
For the final grade the individual assignments count as follows:
i) Three exams: 75%, 25% each
ii) Assignments: 25%

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

To pass the course, a minimum level of 50% has to be reached.

Rating:
[86%; 100%]: 1.0
[74%; 86%): 2.0
[62%;74%): 3.0
[50%; 62%): 4.0
[0; 50%): 5.0

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

Examination topics

1. Instrumental variables
2. Panel model
3. Models with qualitative variables

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:
Heiss, F., “Using R for Introductory Econometrics”, 2016, http://www.urfie.net
Hanck, C., Arnold, M., Gerber, A., and Schmelzer, M., 2019, https://www.econometrics-with-r.org/index.html

R Studio Cloud Projekt Link: https://rstudio.cloud/project/950163

Association in the course directory

Last modified: Mo 26.06.2023 15:46