Table of Contents
Preface.
Acknowledgments.
Chapter 1 Overview of Time Series.
1.1 Introduction
1.2 Analysis Methods and SAS/ETS Software
1.2.1 Options
1.2.2 How SAS/ETS Software Procedures Interrelate
1.3 Simple Models: Regression
1.3.1 Linear Regression
1.3.2 Highly Regular Seasonality
1.3.3 Regression with Transformed Data
Chapter 2 Simple Models: Autoregression.
2.1 Introduction
2.1.1 Terminology and Notation
2.1.2 Statistical Background
2.2 Forecasting
2.2.1 Forecasting with PROC ARIMA
2.2.2 Backshift Notation B for Time Series
2.2.3 Yule-Walker Equations for Covariances
2.3 Fitting an AR Model in PROC REG
Chapter 3 The General ARIMA Model.
3.1 Introduction
3.1.1 Statistical Background
3.1.2 Terminology and Notation
3.2 Prediction
3.2.1 One-Step-Ahead Predictions
3.2.2 Future Predictions
3.3 Model Identification
3.3.1 Stationarity and Invertibility
3.3.2 Time Series Identification
3.3.3 Chi-Square Check of Residuals
3.3.4 Summary of Model Identification
3.4 Examples and Instructions
3.4.1 IDENTIFY Statement for Series 1 - 8
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 8
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
Chapter 4 The ARIMA Model: Introductory Applications.
4.1 Seasonal Time Series
4.1.1 Introduction to Seasonal Modeling
4.1.2 Model Identification
4.2 Models with Explanatory Variables
4.2.1 Case 1: Regression with Time Series Errors
4.2.2 Case 1A: Intervention
4.2.3 Case 2: Simple Transfer Function
4.2.4 Case 3: General Transfer Function
4.2.5 Case 3A: Leading Indicators
4.2.6 Case 3B: Intervention
4.3 Methodology and Example
4.3.1 Case 1: Regression with Time Series Errors
4.3.2 Case 2: Simple Transfer Functions
4.3.3 Case 3: General Transfer Functions
4.3.4 Case 3B: Intervention
4.4 Further Examples
4.4.1 North Carolina Retail Sales
4.4.2 Construction Series Revisited
4.4.3 Milk Scare (Intervention)
4.4.4 Terrorist Attack
Chapter 5 The ARIMA Model: Special Applications.
5.1 Regression with Time Series Errors and Unequal Variances
5.1.1 Autoregressive Errors
5.1.2 Example: Energy Demand at a University
5.1.3 Unequal Variances
5.1.4 ARCH, GARCH, and IGARCH for Unequal Variances
5.2 Cointegration
5.2.1 Introduction
5.2.2 Cointegration and Eigenvalues
5.2.3 Impulse Response Function
5.2.4 Roots in Higher-Order Models
5.2.5 Cointegration and Unit Roots
5.2.6 An Illustrative Example
5.2.7 Estimating the Cointegrating Vector
5.2.8 Intercepts and More Lags
5.2.9 PROC VARMAX
5.2.10 Interpreting the Estimates
5.2.11 Diagnostics and Forecasts
Chapter 6 State Space Modeling.
6.1 Introduction
6.1.1 Some Simple Univariate Examples
6.1.2 A Simple Multivariate Example
6.1.3 Equivalence of State Space and Vector ARMA Models
6.2 More Examples
6.2.1 Some Univariate Examples
6.2.2 ARMA(1,1) of Dimension 2
6.3 PROC STATESPACE
6.3.1 State Vectors Determined from Covariances
6.3.2 Canonical Correlations
6.3.3 Simulated Example
Chapter 7 Spectral Analysis.
7.1 Periodic Data: Introduction
7.2 Example: Plant Enzyme Activity
7.3 PROC SPECTRA Introduced
7.4 Testing for White Noise
7.5 Harmonic Frequencies
7.6 Extremely Fast Fluctuations and Aliasing
7.7 The Spectral Density
7.8 Some Mathematical Detail (Optional Reading)
7.9 Estimating the Spectrum: The Smoothed Periodogram
7.10 Cross-Spectral Analysis
7.10.1 Interpreting Cross-Spectral Quantities
7.10.2 Interpreting Cross-Amplitude and Phase Spectra
7.10.3 PROC SPECTRA Statements
7.10.4 Cross-Spectral Analysis of the Neuse River Data
7.10.5 Details on Gain, Phase, and Pure Delay
Chapter 8 Data Mining and Forecasting.
8.1 Introduction
8.2 Forecasting Data Model
8.3 The Time Series Forecasting System
8.4 HPF Procedure
8.5 Scorecard Development
8.6 Business Goal Performance Metrics
8.7 Graphical Displays
8.8 Goal-Seeking Model Development
8.9 Summary
References.
Index.