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:

  1. Introduction and Descriptive Methods
  2. Stationary Stochastic Processes (AR, MA, ARMA)
  3. Estimation, Specification and Validation of ARMA models
  4. Nonstationary Processes (ARIMA, Unit Root Tests)
  5. Forecasting
  6. Time Series Models of Heteroskedasticity (ARCH + GARCH Processes)
  7. 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: