STAT 520
                               Forecasting and Time Series


Course Description

 Roumen Vesselinov, PhD
 Department of Statistics
 Course webpage
  Office  LeConte 200-C
  Phone (803) 777-5074
Purpose This is a course in applied time series analysis and forecasting. Students will have hands-on experience with applied analysis based on actual time series from economics, business cycle, finance, management, and science.
Offered on alternating Spring semesters.
Prerequisites STAT 516 or MGSC 391; or students must have some understanding of regression type models.
Talk to the instructor to get a permission to register for the course.
Textbook Introduction to Time Series and Forecasting,
2nd edition, by P.J. Brockwell and R.A. Davis, Springer, 2002.
Software ITSM2000 - Comes free with the textbook with no exparation date. Specialized software for time series analysis and forecasting. User friendly with pull down menus and good graphical capabilities.
Grading Final grade is weighted average:
Exam 1 (25%), Exam 2 (25%), Final (30%), Project (10%), HW (10%).
 Grading (%):
 A 93; B+ 88; B 83; C+ 78; C 70;
 D+ 67; D 60; F <60;
Distance Ed.
      Courses            Video
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
   - add-factor approach
   - simultaneous equations models
15. Simulations
* Topics subject to change.

Designed by  _Sun4o_