Syllabus
- Introduction to Stochastic Processes
- Autoregressive Moving Average Processes
- Estimation of Vector ARMA Models
- Prediction
- Testing for Causality
- Innovations Accounting
- Structural VAR
Targeted Audience:
Advanced students and PhD students in econometrics, statistics, VWL, BWL, mathematics or computer science.
Prerequisites:
Profound knowledge in matrix-algebra and econometrics (econometrics I)
or statistics (linear models). Basic knowledge in univariate time series
analysis is not demanded but of advantage.
Record of achievement:
This course consists of two parts. The first part of „Multivariate Time
Series Analysis“ is equivalent to the lecture „Multivariate
Zeitreihen/Multivariate Time Series“ (3 ECTS-Credits) for statisticians;
the second part can be recognised as „Ausgewählte Gebiete der
theoretischen Statistik B/Selected Topics in theoretic Statistics B“ (3
ECTS-Credits). For each part there will be a separate exam that takes
one hour. That is, you only have to attend the first part (see below) of
the course if you want to obtain credit points for the course
“Multivariate Zeitreihen”. Apart from that, the whole course is
equivalent to "Time-Series Econometrics" and counts as a class for Ph.D.
candidates in economics. Both exams constitute the exam you have to
pass in order to obtain the "Schein" for "Time-Series Econometrics".
Literature:
- Lütkepohl, H., New Introduction to Multiple Time Series Analysis, New York: Springer-Verlag, 2005
- Brockwell, P. J. and Davis, R. A. (1987), Time Series: Theory and Methods (2nd edition)
- Hamilton, J. D. (1994), Time Series Analysis, Princeton University Press
- Lütkepohl, H., Krätzig, M. (2004), Applied Time Series Econometrics, Cambridge University Press
- Rinne, H. and Specht, K. (2002), Statistische Modellierung, Schätzung und Prognose, Vahlen
- Tsay, R. S. (2005), Analysis of Financial Time Series (2nd edition), Wiley-Interscience
- Wei, W. W. S.(2005), Time Series Analysis: Univariate and Multivariate Methods (2nd edition), Addison-Wesley
- Учитель: Dennis Mao