123241-44 | N Advanced Methods | N | Limited Dependent Variables

Veranstaltungsdetails

Lehrende: Dr. Hugo Bodory

Veranstaltungsart: Vorlesung mit Übungsanteilen

Orga-Einheit: Corporate Management & Economics

Anzeige im Stundenplan: Advanced Methods | N

Semesterwochenstunden: 1,5

Credits: 3,0

Standort: Campus der Zeppelin Universität

Unterrichtssprache: Englisch

Min. | Max. Teilnehmerzahl: 5 | 30

Prioritätsschema: Standard-Priorisierung

Inhalte:
This course presents methods for the analysis of data with dependent outcome variables that are limited in their range. Such limitations may arise due to models with binary, ordinal, categorical, or censored outcome variables. Models with limited dependent variables are commonly encountered in empirical work that analyzes microdata (e.g. survey data). The topics covered include: 

1.     Probability models.
2.     Maximum likelihood estimation.
3.     Binary, multinomial, and ordered response models.
4.     Tobit, sample selection, and treatment effect models.
5.     Duration and count data models.

As practical illustration and for further intuition, some of methods discussed in the lectures will be applied to data using the statistical software “R (Rstudio)”.


Prerequisites:

a)     Basic knowledge of econometric methods (OLS model).
b)     Basic knowledge of the programming language “R (Rstudio)”. If you have no experience with R, the tutorial provided at https://www.statmethods.net/r-tutorial/index.html may be helpful.

Lernziele:
Students should get familiar with models based on limited dependent variables and be able to practically implement the concepts.

Weitere Informationen zu den Prüfungsleistungen:
A 10-page midterm paper has to be submitted 2 weeks after the end of the last lecture. This paper accounts for hundred percent of the grade.

Literatur:
Winkelmann R., Boes S. (2009): Analysis of Microdata, 2nd edition. New York: Springer-Verlag.
Wooldridge, J. M. (2010): Econometric Analysis of Cross Section and Panel Data, 2nd edition, The MIT Press.
Further literature will be announced in the lectures.

Termine
Datum Von Bis Raum Lehrende
1 Mo, 15. Apr. 2019 10:00 16:00 Fab 3 | 2.02 Dr. Hugo Bodory
2 Di, 16. Apr. 2019 10:00 16:00 Fab 3 | 2.02 Dr. Hugo Bodory
3 Mi, 17. Apr. 2019 10:00 16:00 Fab 3 | 2.02 Dr. Hugo Bodory
4 Do, 18. Apr. 2019 10:00 16:00 Fab 3 | 2.02 Dr. Hugo Bodory
Veranstaltungseigene Prüfungen
Beschreibung Datum Lehrende Bestehenspflicht
1. Midterm k.Terminbuchung Ja
2. Midterm (Wdh.) k.Terminbuchung Ja
Übersicht der Kurstermine
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Lehrende
Dr. Hugo Bodory