Person(s) in Charge
Prof.Dr. Peter Ruckdeschel
time series analysis
lecture (3) + exercise course (1)
Statistics II is helpful but not obligatory
Uni Oldenburg, Tu 12:00-14:00 + Th, 14:00-16:00, at W01 0-006
Autocovariance and partial autocovariance
- stationarity, ergodizity, and mixing concepts
- prediction in the time domain
- Herglotz Theorem; spectral measure/representation of a stationary process
- ARIMA models; state space models; GARCH models
– estimation and inference
– Kalman filter and smoother; EM-Algorithm
The students get to know basic concepts of time series analysis in discrete time and important model families in this context; they learn how to fit such models to real data and how to make inference.
- Durbin, J., Koopman, S.J.: Time series analysis by state space methods. Oxford University Press.
- Brockwell, PJ., Davis, R.A.: Time series: theory and methods. Springer.
- Brockwell, PJ., Davis, R.A.: Introduction to time series and forecasting.
- Hamilton, J.D. Time series analysis. Princeton university press.
- Schlittgen, R., Streitberg, B. Zeitreihenanalyse. Oldenbourg.