# Teaching overview

## Bachelor

### Mathematik

Introductory “Mathematics 101” course covering the basics of: Analysis, elementary financial mathematics, linear algebra, probability calculus.

### Statistische Modellbildung

Statistical modeling course consolidating and extending the skills for exploratory and model-based analyses: Multivariate methods, cluster analysis, independence tests, (generalized) linear models.

## Master

**Time Series Analysis**

Methods course for the analysis and modeling of time series data: Stochastic processes, ARIMA models, stationarity, unit roots, GARCH models, time series regression and structural change.

**Microeconometrics**

Methods course for the analysis and modeling of cross-sectional data: Generalized linear models, binary/multinomial/ordered responses, count data, limited dependent variables, duration models.

## PhD

**R Programming**

Advanced R programming course for statistical models: Model formulation, object orientation and formula interfaces, documentation/packaging/version control, numeric optimization, inference.

## Extracurricular

**R Introduction**

Introductory R course: First steps, installation/documentation/usage, data management, exploratory data analysis and regression, graphics and statistical reports.

**Applied Econometrics with R**

Course materials accompanying the “Applied Econometrics with R” book (Kleiber & Zeileis 2008): Linear regression, diagnostics, microeconometrics, time series, programming basics, financial econometrics.