Es ist eine neuere Version 2026W dieser LV im Curriculum Master's programme Social Economics 2026W vorhanden.
(*) Unfortunately this information is not available in english.
Workload
Education level
Study areas
Responsible person
Hours per week
Coordinating university
4 ECTS
B2 - Bachelor's programme 2. year
Statistics
Helga Wagner
2 hpw
Johannes Kepler University Linz
Detailed information
Pre-requisites
(*)keine
Original study plan
Bachelor's programme Statistics and Data Science 2025W
Learning Outcomes
Competences
Students are able to analyse time series with standard models like ARMA ARIMA and GARCH models, to interprete the results correctly and perform a residual analysis of the fitted models
Skills
Knowledge
Knowing and understanding of the basic problems, terms and methods for the analysis of time series (k1,k2)
Applying and critical evaluation of methods for time series analysis (k3,k4, k5)
Applying methods for time series analysis with the statistic software R (k3)
Implementing and performing simulation studies for time series models (k2,k3)
Basic concepts and descriptive methods for time series analysis
Exponential smoothing
ARMA models for stationary time series
ARIMA models and unit root tests
Modelling volatility with ARCH and GARCH models
Analysis of time series with the statistic software R
Criteria for evaluation
Exam Project
Methods
Lecture Computer lab
Language
German
Study material
Cowpertwait, Paul S. P. and Metcalfe, Andrew V. (2009)
Introductory time series with R
Vogel, Jürgen (2015). Prognose von Zeitreihen
Changing subject?
No
Earlier variants
They also cover the requirements of the curriculum (from - to) 4MSZRKV: KV Time Series Analysis (Statistics) (2011S-2014S)
