STAT 720
 Time Series Analysis
 
 








    

Course Description



Purpose 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. 
Prerequisites STAT 704 and 512. Stochastic properties, identification, estimation, and forecasting methods for stationary and nonstationary time series models. Talk to the instructor to get a permission to register for the course.
Textbook Required: Introduction to Time Series and Forecasting,
               2nd edition, by P.J. Brockwell and R.A. Davis, Springer, 2002.

Recommended:  Time Series: Theory and Methods, 2nd edition, P.J. Brockwell and R.A. Davis,
                         Springer, 1991.
Grading Exam 1 (20%, in class, open book), Exam 2 (20%, in class, open book), Final Exam (30%, take home), Project (20%) and homework problems (10%). Grading (%):
A 90; B+  87; B 80; C+ 77; C 70;
D+ 67; D 60; F <60;
Software ITSM2000 comes free with the textbook.
Specialized software for time series analysis and forecasting.
User friendly with pull down menus and good graphical capabilities.
Selected Topics 1. Box-Jenkins ARIMA
2. Seasonal ARIMA models
3. Unit roots
4. Cointegration
5. Granger causality
6. Vector Autoregression (VAR) models
7. Vector Error Correction (VEC) models
8. Intervention analysis and structural change
9. State-Space models
10. Markov regime-switching model
11. ARCH models
12. GARCH models
13. Smoothing
14. Forecasting techniques
15. Simulations




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