040111 KU Introductory Econometrics (MA) (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
Zusammenfassung
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 09.09.2024 09:00 bis Do 19.09.2024 12:00
- Anmeldung von Mi 25.09.2024 09:00 bis Do 26.09.2024 12:00
- Abmeldung bis Mo 14.10.2024 23:59
An/Abmeldeinformationen sind bei der jeweiligen Gruppe verfügbar.
Gruppen
Gruppe 1
max. 200 Teilnehmer*innen
Sprache: Englisch
Lernplattform: Moodle
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Donnerstag 03.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 04.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 10.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 11.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 17.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 18.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 24.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 25.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 31.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 07.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 08.11. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- N Donnerstag 21.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 28.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 29.11. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 05.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 06.12. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Freitag 06.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 12.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 13.12. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 09.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 10.01. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 16.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 17.01. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Donnerstag 23.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Freitag 24.01. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Mittwoch 12.02. 09:45 - 11:15 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
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.AssessmentThe assessment consists of 2 tests during the semester (midterm, final exam – each 45%) and homework (2 exercises in groups of up to 4, each 5%).The tests will take place on following days:15.11.2024: 13.15-14.45h31.01.2025: 13.15-14.45hThe tests will take 60 minutes. The questions will refer to general material covered in the course, analytical derivations, and interpretations of empirical results. Each test will count for 45% and homework for 10%.Students who either failed (i.e., obtained less than 50%) or missed one of the two exams during the semester are eligible to participate in the retake exam. The retake exam takes place on 12.02.2025. Students who want to participate in the retake exam need to register by 06.02.2025 the latest. The result of the retake exam replaces the worse of the two exams during the semester.
Mindestanforderungen und Beurteilungsmaßstab
To pass the course, a minimum level of 50% has to be reached.Grades:
[87.5%; 100%]:1.0
[75%; 87.5%): 2.0
[62.5%;75%): 3.0
[50%; 62.5%): 4.0
[0; 50%): 5.0Examination language: English.
[87.5%; 100%]:1.0
[75%; 87.5%): 2.0
[62.5%;75%): 3.0
[50%; 62.5%): 4.0
[0; 50%): 5.0Examination language: English.
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.
Greene, W.H. (2019): Econometric Analysis, 8th edition, Pearson.
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.
Greene, W.H. (2019): Econometric Analysis, 8th edition, Pearson.
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.
Gruppe 2
max. 200 Teilnehmer*innen
Sprache: Englisch
Lernplattform: Moodle
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Dienstag 01.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 03.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 08.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 10.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 15.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 17.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 22.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 24.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 29.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 31.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 05.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 07.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 19.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- N Donnerstag 21.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 26.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 28.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 03.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 05.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 10.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 12.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 17.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Dienstag 07.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 09.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 14.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 16.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Dienstag 21.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Donnerstag 23.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Ziele, Inhalte und Methode der Lehrveranstaltung
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 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, which takes place in parallel to the lecture.
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, which takes place in parallel to the lecture.
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.AssessmentThe assessment consists of 2 tests during the semester (midterm, final exam – each 45%) and homework (2 exercises in groups of up to 4, each 5%).The tests will take place on following days:15.11.2024: 13.15-14.45h31.01.2025: 13.15-14.45hThe tests will take 60 minutes. The questions will refer to general material covered in the course, analytical derivations, and interpretations of empirical results. Each test will count for 45% and homework for 10%.Students who either failed (i.e., obtained less than 50%) or missed one of the two exams during the semester are eligible to participate in the retake exam. The retake exam takes place on 12.02.2025. Students who want to participate in the retake exam need to register by 06.02.2025 the latest. The result of the retake exam replaces the worse of the two exams during the semester.
Mindestanforderungen und Beurteilungsmaßstab
To pass the course, a minimum level of 50% has to be reached.Grades:[87.5%; 100%]:1.0
[75%; 87.5%): 2.0
[62.5%;75%): 3.0
[50%; 62.5%): 4.0
[0; 50%): 5.0Examination language: English.
[75%; 87.5%): 2.0
[62.5%;75%): 3.0
[50%; 62.5%): 4.0
[0; 50%): 5.0Examination language: English.
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.
Information
Prüfungsstoff
Examination Topics
All material covered in the course.
All material covered in the course.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mi 20.11.2024 09:05
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.