# I. Syllabus:

The aim of the course is to provide you with the theoretical basis for working with time series models. It starts with an overview of basic notions of time series analysis, and continues with the simple component model distinguishing between deterministic and random components. For the random components, we introduce linear models for the conditional mean (in particular ARMA) and justify them by the Wold decomposition theorem. Estimation and model selection is discussed in detail for the autoregressive process, followed by a brief analysis of selected nonlinear models (including the class of GARCH models for the conditional variance). The course is completed with an introductory discussion of integrated series. This course is the prerequisite for several more specific follow-up courses, ans also for any master thesis under my guidance.

1. Time series vs. stochastic processes
2. The component model and trend filters
3. Linear models (AR/MA)
4. Estimation and forecasting for ARMA models
5. Nonlinear models (Nonlinear AR \& GARCH)
6. (Non)stationary time series (ARIMA)

# III. Method of Assessment:

• Written exam, solving problems similar to those discussed in the tutorial.
• You can earn some bonus points in the computer class.

# VI. Voluntary PC-tutorial:

Access to the computer lab requires a one-time registration with a Stu-Account.

# IX. Literature:

• Brockwell, P.J. and R.A. Davis (2002), Introduction to Time Series and Forecasting, 2nd ed., Springer
• Hamilton, J. (1994), Time Series Analysis, Princeton University Press
• Lütkepohl, H. and M. Krätzig (2004), Applied Time Series Econometrics, Cambridge University Press
• Enders, W. (1995, 2003), Applied Econometric Time Series, Wiley
• Tong, H. (1990), Non-linear Time Series: A dynamical system approach, Oxford University Press
• Brockwell, P. J. and R. A. Davis (1991), Time Series: Theory and Methods, 2nd ed., Springer (more advanced)
• Fan, J. and Yao, Q. (2003), Nonlinear Time Series: Nonparametric and parametric methods, Springer (more advanced)
• Douc, R., E. Moulines and D. S. Stoffer (2014), Nonlinear Time Series: Theory, methods, and applications with R examples (more advanced)
• in German: Schlittgen, R. and B. H. J. Streitberg (2001), Zeitreihenanalyse, 9th ed., Oldenbourg