Course Homepage: Fall 2011

Usually Offered: Alternating Spring Semesters and in MGSC

Purpose: To show the student how to recognize time series data, and to acquaint the student with the peculariaties of this kind of data. The appropriate questions to ask of the data and the general approaches which are particular to these data are examined in detail.

Current Textbook: Time Series Analysis with Applications in R, 2nd edition, by Cryer, J. and Chan, K., Springer, 2008.

 

Topics Covered	Time
Introduction to time series. Autocorrelated data. Stationarity and trends	1 week
Autoregressive, moving average, and autoregressive moving average models. Random walk model. Seasonal models. The B-J notation. Integrated models.	2 weeks
The concept of identification. Autocorrelation and partial autocorrelation function. Behavior of sample estimates	2 weeks
Diagnostic checking. Residual analysis. Result of over and underspecifying the model.	2 weeks
Forecasting. Optimal forecasts and interval forecasts. Forecasting nonstaionary models. Updating the forecasts. Exponential smoothing. Backcasting.	2 weeks
Details of analysis of several real socio-economic data sets.	2 weeks
The transfer function model and their identification. Cross covariance. Pre-whitening. Diagnostics. Forecasting.	2 weeks
Intervention analysis. Identification and estimation. The transfer function model.	1 week

The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.  

Contact Faculty: Joshua Tebbs
(Last Updated: July 24th, 2006)