(6 ECTS/4 Bonus Points)
Lecturer: PD Dr. Stefan Klößner
Lecture | Tutorial | |
---|---|---|
Weekday & Time | Tuesday 08:30 – 10:00 | Monday 10:00 – 11:30 |
Building | C3 1 | C3 1 |
Room | 3.01 | 3.01 |
Start | April 10, 2018 | April 16, 2018 |
Prüfungsmodalitäten40% der Gesamtnote ergeben sich aus der Benotung der Projektarbeit, 60% aus der mündlichen Prüfung am Ende des Semesters. |
The first objective is to provide students with the foundations of stochastic processes in discrete time with a focus on forecasting as a central economic application. Students learn to select and estimate models for time series data, including model diagnostics and statistical tests for the appropriateness of the chosen model family. The objectives include generalizations of methods for univariate linear time series models to nonlinear models or multivariate time series. Major importance is attributed to the practical application of the theoretical concepts with statistical/econometric software.
Course prerequisites include introductory probability theory and statistics, as well as calculus and matrix algebra. Additionally, an introductory econometrics course is very useful.
Stochastic processes in discrete time
Linear prediction of univariate stationary processes
Univariate linear time series models (ARIMA processes)
Estimation and model selection for ARIMA processes
Specification tests
Nonlinear time series models
Multivariate linear time series models (VAR processes)
Cointegration