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The ARIMA Procedure
Overview
Getting Started
The Three Stages of ARIMA Modeling
Identification Stage
Estimation and Diagnostic Checking Stage
Forecasting Stage
Using ARIMA Procedure Statements
General Notation for ARIMA Models
Stationarity
Differencing
Subset, Seasonal, and Factored ARMA Models
Input Variables and Regression with ARMA Errors
Intervention Models and Interrupted Time Series
Rational Transfer Functions and Distributed Lag Models
Forecasting with Input Variables
Data Requirements
Syntax
Functional Summary
PROC ARIMA Statement
BY Statement
IDENTIFY Statement
ESTIMATE Statement
OUTLIER Statement
FORECAST Statement
Details
The Inverse Autocorrelation Function
The Partial Autocorrelation Function
The Cross-Correlation Function
The ESACF Method
The MINIC Method
The SCAN Method
Stationarity Tests
Prewhitening
Identifying Transfer Function Models
Missing Values and Autocorrelations
Estimation Details
Specifying Inputs and Transfer Functions
Initial Values
Stationarity and Invertibility
Naming of Model Parameters
Missing Values and Estimation and Forecasting
Forecasting Details
Forecasting Log Transformed Data
Specifying Series Periodicity
Detecting Outliers
OUT= Data Set
OUTCOV= Data Set
OUTEST= Data Set
OUTMODEL= SAS Data Set
OUTSTAT= Data Set
Printed Output
ODS Table Names
Statistical Graphics
Examples
Simulated IMA Model
Seasonal Model for the Airline Series
Model for Series J Data from Box and Jenkins
An Intervention Model for Ozone Data
Using Diagnostics to Identify ARIMA Models
Detection of Level Changes in the Nile River Data
Iterative Outlier Detection
References
Details: ARIMA Procedure
Subsections:
The Inverse Autocorrelation Function
The Partial Autocorrelation Function
The Cross-Correlation Function
The ESACF Method
The MINIC Method
The SCAN Method
Stationarity Tests
Prewhitening
Identifying Transfer Function Models
Missing Values and Autocorrelations
Estimation Details
Specifying Inputs and Transfer Functions
Initial Values
Stationarity and Invertibility
Naming of Model Parameters
Missing Values and Estimation and Forecasting
Forecasting Details
Forecasting Log Transformed Data
Specifying Series Periodicity
Detecting Outliers
OUT= Data Set
OUTCOV= Data Set
OUTEST= Data Set
OUTMODEL= SAS Data Set
OUTSTAT= Data Set
Printed Output
ODS Table Names
Statistical Graphics
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