Vorlesung Cross Sectional Data Analysis
durchgeführt von Thomas Gautschi
Dienstags 13:45 – 15:15 Uhr in B 317 A 5, 6 Bauteil B
Startdatum: 05.09.2017; Enddatum: 05.12.2017
The main focus lies on the introduction to statistical models and estimators beyond linear regression useful to social scientists. A good understanding of the classical linear regression model is a prerequisite and required for the further topics of the course. We will first discuss violations of the asymptotic properties of the linear regression model and ways to address these violations (heteroscedasticity, endogeneity, proxy variables, IV-estimator). The second part of the class is dedicated to first the maximum likelihood estimator and second to nonlinear models for binary choice decisions (Logit, Probit), ordinal dependant variables, and count data (Poisson, Negative Binomial). Classes will be accompanied by lab sessions to repeat and practice the topics from the classes.