SAS SYSTEM FOR FORECASTING TIME SERIES, SECOND EDITION

Easy-to-read and comprehensive, this book shows how the SAS System performs multivariate time series analysis and features the advanced SAS procedures STATSPACE, ARIMA, and SPECTRA. The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analysed and the reults desired. Other topics covered include detecting sinusoidal components in time series models and performing bivariate corr-spectral analysis and comparing the results with the standard transfer function methodology. The authors? unique approach to integrating students in a variety of disciplines and industries. Emphasis is on correct interpretation of output to draw meaningful conclusions. The volume, co-pubished by SAS and JWS, features both theory and practicality, and accompanies a soon-to-be extensive library of SAS hands-on manuals in a multitude of statistical areas. The book can be used with a number of hardware-specific computing machines including CMS, Mac, MVS, Opem VMS Alpha, Opmen VMS VAX, OS/390, OS/2, UNIX, and Windows.

John C. Brocklebank, Research and Development Director of Analytic Solutions at SAS, joined SAS in 1981 and has been a SAS user since 1978. Dr. Brocklebank received his Ph.D. in statistics and mathematics from North Carolina State University in 1981. He is often invited to conferences to speak about time series and statistical methods.

David A. Dickey is Professor of Statistics at North Carolina State University, where he teaches graduate courses in statistical methods and time series. An accomplished SAS user since 1976 and a prolific author, Dr. Dickey is the co-inventor of the Dickey-Fuller test used in SAS/ETS software. He received his Ph.D. in statistics from Iowa State University in 1976. He is a fellow of the American Statistical Association and a member of the Institute of Mathematical Statistics.

Preface.

Acknowledgments.

Chapter 1- Overview of Time Series.

Chapter 2- Simple Models: Autoregression.

Chapter 3- The General ARIMA Model.

Chapter 4- The ARIMA Model: Introductory Applications.

Chapter 5- The ARIMA Model: Special Applications.

Chapter 6- State Space Modeling.

Chapter 7- Spectral Analysis.

Chapter 8- Data Mining and Forecasting.

References.

Index.