Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures.
Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods:
- ARIMA models
- Vector autoregressive models
- Exponential smoothing models
- Unobserved component and state-space models
- Seasonal adjustment
- Spectral analysis
Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition:
- The ARIMA procedure
- The AUTOREG procedure
- The VARMAX procedure
- The ESM procedure
- The UCM and SSM procedures
- The X13 procedure
- The SPECTRA procedure
- SAS Forecast Studio
Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs.
This book is part of the SAS Press program.
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About the Author
David A. Dickey, PhD, is a William Neal Reynolds Distinguished 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, an award-winning teacher, and a prolific and highly cited author, he co-invented the famous Dickey-Fuller test used in SAS/ETS software. He is a fellow of the American Statistical Association, was a founding member of the NCSU Institute for Advanced Analytics, is a member of the Financial Math faculty, and fulfills an associate appointment in the Department of Agricultural and Resource Economics. Dr. Dickey holds an MS in mathematics from Miami University–Ohio, and in 1976 he received his PhD in statistics from Iowa State University.
Bong S. Choi, PhD, is a Senior Associate Analytical Consultant at SAS. He has worked on projects across a variety of industries, including health care, retail, and banking. A SAS certified advanced programmer, he has been a SAS user since 2011. Dr. Choi holds an MS in applied statistics from the University of Michigan at Ann Arbor and in 2016 received his PhD in statistics from North Carolina State University.