||To acquaint graduate students from various
disciplines with a firm understanding of the ARIMA(p,d,q) class of
models and to use this information to fit appropriate models to real
data and forecast if desired; to familiarize students with the
frequency domain approach including the definition, interpretation and
estimation of the spectral density.
||STAT 704 and 512. Stochastic properties,
identification, estimation, and forecasting methods for stationary and
nonstationary time series models. Talk
instructor to get a permission to register for the course.
to Time Series and Forecasting,
2nd edition, by P.J. Brockwell and R.A. Davis, Springer, 2002.
Time Series: Theory and Methods, 2nd edition, P.J. Brockwell and R.A.
||Exam 1 (20%, in
class, open book),
Exam 2 (20%, in class, open book), Final Exam
(30%, take home), Project (20%) and homework problems
A 90; B+ 87; B
80; C+ 77; C 70;
D+ 67; D 60; F
||ITSM2000 – comes free with the
software for time series analysis and forecasting.
friendly with pull down menus and good graphical capabilities.
2. Seasonal ARIMA models
3. Unit roots
5. Granger causality
6. Vector Autoregression (VAR) models
7. Vector Error Correction (VEC) models
8. Intervention analysis and structural change
9. State-Space models
Markov regime-switching model
12. GARCH models
14. Forecasting techniques