312508 Econometric Time Series Analysis (V) (SoSe 2026)

This course introduces time series modeling with an emphasis on econometric models. Starting from
the component model we focus on autoregressive modelling. This is generalized to ARIMA models in state
space representation. We discuss filtering for time series, in particular the celebrated Kalman filter.

Based on this model class conditional heteroskedasticity and unit roots extend the modeling capabilities to cover a
broad range of typical features of time series from economics and finance.

There is no Übung accompanying the lecture. Instead the lectures will contain hands-on experiences using R to estimate
time series models in class.

Requirements for participation, required level

The course is based on basic statistics lectures. Attendants should have a profound knowledge
on the linear regression model as well as likelihood estimation.
No prior knowledge on time series analysis is required.

Bibliography

K. Neusser (2015): Time Series Analysis in Economics. Springer.