040111 KU Introductory Econometrics (MA) (2023W)
Prüfungsimmanente Lehrveranstaltung
Labels
VOR-ORT
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 11.09.2023 09:00 bis Fr 22.09.2023 12:00
- Anmeldung von Di 26.09.2023 09:00 bis Mi 27.09.2023 12:00
- Abmeldung bis Fr 20.10.2023 23:59
Details
max. 200 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Dienstag
03.10.
16:45 - 18:15
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
05.10.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
10.10.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
12.10.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
17.10.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
19.10.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
24.10.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
31.10.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
07.11.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
09.11.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
16.11.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
21.11.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
23.11.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
28.11.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
30.11.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
05.12.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
05.12.
18:30 - 20:00
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Mittwoch
06.12.
18:30 - 20:00
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Donnerstag
07.12.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
12.12.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
14.12.
16:45 - 18:15
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
Dienstag
09.01.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
11.01.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
16.01.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
18.01.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
23.01.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Donnerstag
25.01.
16:45 - 18:15
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Dienstag
30.01.
18:30 - 20:00
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Unexcused absence from the first session will automatically lead to deregistration in order to allow students on the waiting list to move up. If you are unable to attend the first session, you must notify me in advance via email in order to continue attending the course.Assessment (subject to changes)
The assessment consists of 3 tests during the semester (approximately after every 4th to 5th week).
The tests will take place on following sessions:
14.11.2023
14.12.2023
01.02.2024
The tests will take approximately 45-60 minutes. The questions will refer to general material covered in the course, analytical derivations, and interpretations of empirical results.Each test will count 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.
The assessment consists of 3 tests during the semester (approximately after every 4th to 5th week).
The tests will take place on following sessions:
14.11.2023
14.12.2023
01.02.2024
The tests will take approximately 45-60 minutes. The questions will refer to general material covered in the course, analytical derivations, and interpretations of empirical results.Each test will count 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.
Mindestanforderungen und Beurteilungsmaßstab
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%): 5.0Examination language: English.
[85%; 100%]: 1.0
[70%; 85%): 2.0
[55%;70%): 3.0
[45%; 55%): 4.0
[0; 45%): 5.0Examination language: English.
Prüfungsstoff
Examination Topics
All material covered in the course.
All material covered in the course.
Literatur
Main books:
Stock, J. H., and Watson, M. W. (2020), Introduction to Econometrics, Global Edition. Pearson Education Limited
Wooldridge, Jeffrey M. Introductory econometrics: A modern approach. 7th edition, Cengage learning, 2020.Additional books:
Angrist, J.D. and Pischke, J.-S. (2009): Mostly Harmless Econometrics: An Empiricst's Companion, Princeton University Press.
Cunningham, Scott. Causal inference: The mixtape. Yale university press, 2021.
Wooldridge, Jeffrey M. Econometric analysis of cross section and panel data. MIT press, 2010.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.
Stock, J. H., and Watson, M. W. (2020), Introduction to Econometrics, Global Edition. Pearson Education Limited
Wooldridge, Jeffrey M. Introductory econometrics: A modern approach. 7th edition, Cengage learning, 2020.Additional books:
Angrist, J.D. and Pischke, J.-S. (2009): Mostly Harmless Econometrics: An Empiricst's Companion, Princeton University Press.
Cunningham, Scott. Causal inference: The mixtape. Yale university press, 2021.
Wooldridge, Jeffrey M. Econometric analysis of cross section and panel data. MIT press, 2010.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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mi 24.01.2024 08:45
The course is a first-year master-level course in econometrics for students who already have a background in statistics and are familiar with the basic principles of probability theory, mathematical statistics and linear regression. The course provides an understanding of standard 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.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, experiments and quasi-experiments, and big data among others.If not compulsory, it is highly recommended to also attend the weekly TA session (Uebung: https://ufind.univie.ac.at/en/course.html?lv=040115&semester=2023W ), which takes place in parallel to the lecture.