Carnegie Mellon University
Tepper School of Business
Business Forecasting with Time Series Models
Professor: Fallaw Sowell
Office: GSIA 313 (Old Building)
Tuesday & Thursday 3:30pm - 5:20pm
rm 153 Posner Hall
6:30pm - 9:30pm
rm 151 Posner Hall
1. Introduction to Time Series Analysis and Forecasting with Applications of SAS and SPSS,
by Robert Yaffee with Monnie McGee, ISBN 0-12-767870-0.
2. SAS for Forecasting Time Series, second edition, by John C. Brocklebank and David A.
Dickey, ISBN 0-471-9566-8.
3. I am not requiring a textbook for SAS. If you would like an introductory book, I would rec-
ommend The Little SAS Book, third edition, by Lora D. Delwiche and Susan J. Slaughter,
4. SAS documentation is available at
The three books are available at the CMU bookstore.
Students will be able to take a new time series and determine its trend and seasonal charac-
teristics. They will be able to determine if the series has conditional heteroskedasticity.
After accounting for trend and seasonal characteristics, the students will be able to estimate
an ARMA model and when appropriate, an ARCH or GARCH model. For these estimated
models the students will be able to make forecasts and summarize the uncertainty inherent in the
OBJECTIVE AND OVERVIEW
This course is an introduction to the basic time series models. The course uses SAS to create
forecasts. The forecasts are constructed from the estimated summary statistics and parameters of
time series models: mainly ARIMA but also ARCH and GARCH. The students should be able to
interpret the uncertainty in the forecasts and in the estimated parameters. Diagnostic statistics
and model selection criteria are presented.
Your course grade will be determined by a paper you write during the mini. You will apply
different estimation techniq