Warning! The directory is not yet complete and will be amended until the beginning of the term.
040111 KU Introductory Econometrics (MA) (2024W)
Continuous assessment of course work
Labels
Summary
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).
- Registration is open from Mo 09.09.2024 09:00 to Th 19.09.2024 12:00
- Registration is open from We 25.09.2024 09:00 to Th 26.09.2024 12:00
- Deregistration possible until Mo 14.10.2024 23:59
Registration information is available for each group.
Groups
Group 1
max. 200 participants
Language: English
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 03.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 04.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 10.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 11.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 17.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 18.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 24.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 25.10. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 31.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 07.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 08.11. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 21.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 22.11. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 28.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 29.11. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 05.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 06.12. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Friday 06.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 12.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 13.12. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 09.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 10.01. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 16.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 17.01. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Thursday 23.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Friday 24.01. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
-
N
Wednesday
12.02.
09:45 - 11:15
Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Aims, contents and method of the course
Assessment and permitted materials
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.
Minimum requirements and assessment criteria
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.
Reading list
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.
Group 2
max. 200 participants
Language: English
LMS: Moodle
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 01.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 03.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 08.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 10.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 15.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 17.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 22.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 24.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 29.10. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 31.10. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 05.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 07.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 19.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 21.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 26.11. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 28.11. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 03.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 05.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 10.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 12.12. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 17.12. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 07.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 09.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 14.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 16.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
- Tuesday 21.01. 15:00 - 16:30 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 23.01. 15:00 - 16:30 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Aims, contents and method of the course
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.
Assessment and permitted materials
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.
Minimum requirements and assessment criteria
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.
Reading list
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
Examination topics
Examination Topics
All material covered in the course.
All material covered in the course.
Association in the course directory
Last modified: Tu 14.01.2025 10:25
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.