Zeitreihenanalyse

Submitted by ruckdeschel on Mon, 01/27/2020 - 19:56
Person(s) in Charge
Prof.Dr. Peter Ruckdeschel
Topic
time series analysis
Type
lecture (3) + exercise course (1)
KP
6KP
Workload
180h
Hours/Week
4.00h
Exam Type
oral exam
Access Limits
None
Prerequisites
Statistics II is helpful but not obligatory
Location
Uni Oldenburg, Tu 12:00-14:00 + Th, 14:00-16:00, at W01 0-006
Description

Contents

  • 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

Goals/Competencies:

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.

References

  • 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.
Semester