040217 KU Data Analysis on Organization and Personell (MA) (2020S)
Prüfungsimmanente Lehrveranstaltung
Labels
service email address: opim.bda@univie.ac.at
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 10.02.2020 09:00 bis Mi 19.02.2020 12:00
- Anmeldung von Di 25.02.2020 09:00 bis Mi 26.02.2020 12:00
- Abmeldung bis Do 30.04.2020 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
learning at home and e-learning
Dear Students,As from 11 March 2020 until 3 April 2020, no courses/exams with student attendance will take place at the University of Vienna. Teaching will instead take place in the form of learning at home and e-learning. Please check on Moodle for the learning at home material, such as lecture notes, execises do files, papers and if you would like to practice with these exercises, these is Stata student Version online.please check Moodle and your university email account for any updated information.
Mittwoch
04.03.
13:15 - 16:30
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Mittwoch
11.03.
13:15 - 16:30
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Mittwoch
18.03.
13:15 - 16:30
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Mittwoch
25.03.
13:15 - 16:30
PC-Seminarraum 1 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag
30.03.
09:45 - 13:00
Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Montag
30.03.
13:15 - 20:00
Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Your final grade is determined by your performance on the quizzes, assignments, presentations, final essay and class participation.
Make-up exams will not be given unless the student has a medical or other serious reason.
Students should hand in homework assignments before class on the day they are due. Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Make-up exams will not be given unless the student has a medical or other serious reason.
Students should hand in homework assignments before class on the day they are due. Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Mindestanforderungen und Beurteilungsmaßstab
Basic knowledge of Business Mathematics and Statistics are required.
Your final grade is determined by your performance on the quizzes, assignments, presentations, final essay and class participation.
Passing grades can generally not be earned by students who miss more than 20% of the total class-time.
Make-up exams will not be given unless the student has a medical or other serious reason.Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Your final grade is determined by your performance on the quizzes, assignments, presentations, final essay and class participation.
Passing grades can generally not be earned by students who miss more than 20% of the total class-time.
Make-up exams will not be given unless the student has a medical or other serious reason.Assignments will be distributed in class or on line. each student must write up his or her answers separately.Exam review is possible during regular semester time by appointment.
Prüfungsstoff
assignments, presentation, final essay, class attendance and participation.
Literatur
Wooldridge „Introductory Econometrics“
– Chapters 1-8, 15, 17
Peter Kennedy „A guide to Econometrics“
– Chapters 1-12, 16, 17
Kohler/Kreuter „Data Analysis using Stata“
– Chapter 8 and 9
– Chapters 1-8, 15, 17
Peter Kennedy „A guide to Econometrics“
– Chapters 1-12, 16, 17
Kohler/Kreuter „Data Analysis using Stata“
– Chapter 8 and 9
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 07.09.2020 15:19
Goal: Upon completion of the course, students should be able to undertake regression analysis and inference on a variety of hypotheses involving cross-sectional and time series data.
This course introduces students to regression tools for analyzing data in economics, finance and related disciplines. Extensions include regression with discrete random variables, instrumental variables regression, quasi-experiments, and regression with time series data. The objective of the course is for the student to learn how to conduct – and how to critique – empirical studies in economics, finance and related fields. Accordingly, the emphasis of the course is on empirical applications. The mathematics of econometrics will be introduced only as needed and will not be a central focus.