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Instructors: Prof. Dr. Vera Eva Tröger
Event type:
Seminar
Displayed in timetable as:
VRS1:
Hours per week:
2
Credits:
6,0
Language of instruction:
English
Min. | Max. participants:
10 | 20
Comments/contents:
This course will cover various questions and specification issues in multivariate quantitative data analysis and is designed for students who already have training in basic statistics and knowledge of bivariate linear regression analysis. The course deals with different problems arising in applied data analysis when multiple violations of the basic regression assumptions occur. We will start by discussing the basic Gauss-Markov assumptions of OLS regression analysis, their violations and suitable solutions to such misspecifications, especially when they occur in conjunction. Thus, participants will learn how to deal with different types of heteroskedasticity, spatial correlation, serial correlation and dynamics as well as various kinds of heterogeneity. This discussion will include working with divers data such as cross-sectional, time-series, panel and pooled data. The course gives an overview of the problems arising from complex data structures and also provides techniques to control and account for specific complications. We will also look at problems arising from non-linear relationships, interactions effects and parameter instability. In addition, this course shows how to deal with specification problems such as complex error structures, different kinds of heterogeneity (e.g. unit and slope), dynamic specification issues, missing data, spatial heterogeneity and dependency. Furthermore, we will look at different data generating processes and adequate estimation procedures for e.g. binary choice and limited dependent variable models. Specifically, we will consider truncated and censored data as well as sample selection, instrumental variable approaches and seemingly unrelated as well as simultaneous equation models. The course combines a more theoretical introduction into different topics with practical analysis of diverse data sets using STATA. Students are encouraged to bring their own data sets and present their research projects and empirical analysis during the course.
Learning objectives:
The course requires basic knowledge of inferential statistics, calculus and linear algebra and is designed to further develop the understanding of statistical problems arising from complex data generating processes in applied data analysis. The course mostly deals with questions of specification and model choice and is therefore a very practical course which should enable students to link their empirical models closer to their theoretical arguments and make model choices that are adequate for the data structure at hand. The taught material should help participants to solve their own estimation problems and increase the reliability and efficiency of statistical results. The course is targeted at social and political scientists as well as economists with average statistical skills with a strong interest in applied empirical research and data analysis. The focus lies on practical problems of applied data analysis.
Didactic concept:
Each topic will have a theoretical/statistical component and a practical session applying the theoretical concepts to real data using STATA.
Information and material for this course will be provided via OpenOlat.
Literature:
- Clark, William Roberts, Matt Golder, and Sona Nadenichek Golder, 2017. Principles of Comparative Politics. 3rd edition. CQ Press.
- Kellstedt, P.M. and Whitten, G.D., 2018. The fundamentals of political science research. Cambridge University Press.
- Angrist, J.D. and Pischke, J.S., 2008. Mostly harmless econometrics: An empiricist's companion. Princeton university press.
- Wooldridge, J.M., 2016. Introductory econometrics: A modern approach. Nelson Education.
- Dougherty, Christopher 2002: Introduction to Econometrics, Second Edition, Oxford University Press.
- Wooldridge, Jeffrey M. 2003: Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge.
- Cameron, Colin A. and Pravin K. Trivedi 2009: Microeconometrics Using Stata, Stata Press.
- Gould, William; Pitblado, Jeffrey and William Sribney 2006: Maximum Likelihood Estimation with Stata, Third Edition, Stata Press.
- Long, Scott J. and Jeremy Freese 2006: Regression Models for Categorical Dependent Variables using Stata, Second Edition, Stata Press.
Additional examination information:
Leistungsanforderungen:
- FSB WiSe 14/15 (Masterzulassung ab 2014), Modul Vergleichende und Regionalstudien (VRS 1): Studienleistungen (siehe A) und ggf. Hausarbeit (siehe B)
- Wahlbereich: Studienleistungen (siehe A)
Zusätzliche Hinweise zu Prüfungen:
A) Studienleistungen (unbenotet):
1 x Homework
B) Prüfungsleistung:
Prüfungsart: Take-Home-Exam
Bewertungsschema: benotet (RPO)
Umfang: ca. 5-10 Seiten
Abgabetermin: tba
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