Applied time series analysis
News:
- Note: The first lecture is on April 8th
- The lectures and tutorials are planned as on-campus sessions.
- Note: Sign-up for this course on ZEUS.
- Further information related will be provided via ILIAS .
Instructors:
Lecture: Prof. Dr. Ralf Brüggemann
Tutorial: Tilmann Härtl
Language:
Lectures and Tutorials are in English
Lectures:
Time: Monday, 11:45 – 13:15
Friday, 11:45 – 13:15, fortnightly (begins 12.04.24)
Room: Monday, tba
Friday, D301
Tutorials:
Time: Friday, 11:45 - 13:15, fortnightly (begins 19.04.24)
Room: D301
Course Description:
Many economic variables are observed over time on a regular frequency (e.g. the quarterly growth rate of GDP, the monthly CPI inflation rate, daily interest rates, daily returns of the DAX stock market index). This type of data is known as time series data and often features correlation over time that can be exploited for forecasting. In this course econometric models for univariate time series data are introduced. Estimation and model specification as well as their use in forecasting is discussed in the lecture. Students learn how to apply these methods in practice during the computer session using Python.
Prerequisites: Bachelor level econometrics
Preliminary Course Outline:
- Introduction and Descriptive Methods
- Stationary Stochastic Processes (AR, MA, ARMA)
- Estimation, Specification and Validation of ARMA models
- Nonstationary Processes (ARIMA, Unit Root Tests)
- Forecasting
- Time Series Models of Heteroskedasticity (ARCH + GARCH Processes)
- Topics in Applied Time Series Modelling
Readings:
- Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press, Chapters 1-3, 4, 5, 15, 17, 21
- Lütkepohl, H. & Krätzig, M. (2004), Applied Time Series Econometrics, Cambridge University Press, Chapters 1, 2, 5, 6
- Enders, W. (2015), Applied Econometric Time Series, 4th edition, Wiley, Chapters 2, 3 ,4
- Further resources to be discussed in class.
Software:
Python will be used in computer tutorials, for programming assignments and take home examination.
Course Material:
- via ILIAS