Book description
To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediatetoadvanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications.Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the wellknown 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 uptodate statistical methods:
 ARIMA models
 Vector autoregressive models
 Exponential smoothing models
 Unobserved component and statespace 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 uptodate 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.
Table of contents
 About This Book
 About The Authors
 Acknowledgments
 Chapter 1: Overview of Time Series
 Chapter 2: Simple Models: Autoregression

Chapter 3: The General ARIMA Model
 3.1 Introduction
 3.2 Prediction
 3.3 Model Identification

3.4 Examples and Instructions
 3.4.1 IDENTIFY Statement for Series 18
 3.4.2 Example: Iron and Steel Export Analysis
 3.4.3 Estimation Methods Used in PROC ARIMA
 3.4.4 ESTIMATE Statement for Series 8A
 3.4.5 Nonstationary Series
 3.4.6 Effect of Differencing on Forecasts
 3.4.7 Examples: Forecasting IBM Series and Silver Series
 3.4.8 Models for Nonstationary Data
 3.4.9 Differencing to Remove a Linear Trend
 3.4.10 Other Identification Techniques
 3.5 Summary of Steps for Analyzing Nonseasonal Univariate Series
 Chapter 4: The ARIMA Model: Introductory Applications

Chapter 5: The ARIMA Model: Special Applications
 5.1 Regression with Time Series Errors and Unequal Variances

5.2 Cointegration
 5.2.1 Cointegration and Eigenvalues
 5.2.2 Impulse Response Function
 5.2.3 Roots in HigherOrder Models
 5.2.4 Cointegration and Unit Roots
 5.2.5 An Illustrative Example
 5.2.6 Estimation of the Cointegrating Vector
 5.2.7 Intercepts and More Lags
 5.2.8 PROC VARMAX
 5.2.9 Interpretation of the Estimates
 5.2.10 Diagnostics and Forecasts
 Chapter 6: Exponential Smoothing
 Chapter 7: Unobserved Components and State Space Models
 Chapter 8: Adjustment for Seasonality with PROC X13
 Chapter 9: SAS Forecast Studio

Chapter 10: Spectral Analysis
 10.1 Introduction
 10.2 Example: Plant Enzyme Activity
 10.3 PROC SPECTRA
 10.4 Tests for White Noise
 10.5 Harmonic Frequencies
 10.6 Extremely Fast Fluctuations and Aliasing
 10.7 The Spectral Density
 10.8 Some Mathematical Detail (Optional Reading)
 10.9 Estimation of the Spectrum: The Smoothed Periodogram
 10.10 CrossSpectral Analysis
 References
 Index
Product information
 Title: SAS for Forecasting Time Series, Third Edition, 3rd Edition
 Author(s):
 Release date: March 2018
 Publisher(s): SAS Institute
 ISBN: 9781629605449
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