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SAS for Forecasting Time Series

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ISBN-10: 1590471822

ISBN-13: 9781590471821

Edition: 2nd 2003

Authors: John C. Brocklebank, David A. Dickey

List price: $68.95
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Book details

List price: $68.95
Edition: 2nd
Copyright year: 2003
Publisher: SAS Institute
Publication date: 1/1/2004
Binding: Hardcover
Pages: 424
Size: 8.25" wide x 10.75" long x 1.00" tall
Weight: 2.134
Language: English

Overview of Time Series
Analysis Methods and SAS/ETS Software
How SAS/ETS Software Procedures Interrelate
Simple Models: Regression
Linear Regression
Highly Regular Seasonality
Regression with Transformed Data
Simple Models: Autoregression
Terminology and Notation
Statistical Background
Forecasting with PROC ARIMA
Backshift Notation B for Time Series
Yule-Walker Equations for Covariances
Fitting an AR Model in PROC REG
The General ARIMA Model
Statistical Background
Terminology and Notation
One-Step-Ahead Predictions
Future Predictions
Model Identification
Stationarity and Invertibility
Time Series Identification
Chi-Square Check of Residuals
Summary of Model Identification
Examples and Instructions
IDENTIFY Statement for Series 1-8
Example: Iron and Steel Export Analysis
Estimation Methods Used in PROC ARIMA
ESTIMATE Statement for Series 8
Nonstationary Series
Effect of Differencing on Forecasts
Examples: Forecasting IBM Series and Silver Series
Models for Nonstationary Data
Differencing to Remove a Linear Trend
Other Identification Techniques
The ARIMA Model: Introductory Applications
Seasonal Time Series
Introduction to Seasonal Modeling
Model Identification
Models with Explanatory Variables
Case 1: Regression with Time Series Errors
Case 1A: Intervention
Case 2: Simple Transfer Function
Case 3: General Transfer Function
Case 3A: Leading Indicators
Case 3B: Intervention
Methodology and Example
Case 1: Regression with Time Series Errors
Case 2: Simple Transfer Functions
Case 3: General Transfer Functions
Case 3B: Intervention
Further Examples
North Carolina Retail Sales
Construction Series Revisited
Milk Scare (Intervention)
Terrorist Attack
The ARIMA Model: Special Applications
Regression with Time Series Errors and Unequal Variances
Autoregressive Errors
Example: Energy Demand at a University
Unequal Variances
ARCH, GARCH, and IGARCH for Unequal Variances
Cointegration and Eigenvalues
Impulse Response Function
Roots in Higher-Order Models
Cointegration and Unit Roots
An Illustrative Example
Estimating the Cointegrating Vector
Intercepts and More Lags
Interpreting the Estimates
Diagnostics and Forecasts
State Space Modeling
Some Simple Univariate Examples
A Simple Multivariate Example
Equivalence of State Space and Vector ARMA Models
More Examples
Some Univariate Examples
ARMA(1,1) of Dimension 2
State Vectors Determined from Covariances
Canonical Correlations
Simulated Example
Spectral Analysis
Periodic Data: Introduction
Example: Plant Enzyme Activity
Testing for White Noise
Harmonic Frequencies
Extremely Fast Fluctuations and Aliasing
The Spectral Density
Some Mathematical Detail (Optional Reading)
Estimating the Spectrum: The Smoothed Periodogram
Cross-Spectral Analysis
Interpreting Cross-Spectral Quantities
Interpreting Cross-Amplitude and Phase Spectra
Cross-Spectral Analysis of the Neuse River Data
Details on Gain, Phase, and Pure Delay
Data Mining and Forecasting
Forecasting Data Model
The Time Series Forecasting System
HPF Procedure
Scorecard Development
Business Goal Performance Metrics
Graphical Displays
Goal-Seeking Model Development