Learning objectives: Students understand the formal foundations of mathematical and statistical theory underlying the linear regression model and some of its generalizations. Given an empirically testable hypothesis and available data, they can choose an adequate, but simple econometric methodology (regression model, estimator, test) to test the empirical hypothesis, and can formally justify their choices.
Recommended module or comparable previous knowledge: Modules “Stastistik I” and “Statistik II” or modules with similar learning outcomes.
Lecture: Estimation and testing in the classical (normal) linear regression model, robust covariance matrix estimation, instrumental variable estimation, estimationof binary response models, maximum likelihood, ARMA models and forecasting, error component panel model, fixed effects and random effects estimators.
Exercise: Topics to be covered include: Introduction to a programming language suitable for working with econometric models. Illustration of the concepts discussed in the lecture using simulation exercises and paper-pencil practice exercises.
StO/PO BA BWL und VWL 2016: 6 LP, Modul: "Foundations of Econometrics"
Klausur (90 min)
Die Veranstaltung wurde 5 mal im Vorlesungsverzeichnis SoSe 2025 gefunden:
