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| The ARIMA Procedure |
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- Overview: ARIMA Procedure
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Getting Started: ARIMA Procedure
- 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: ARIMA Procedure
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Details: ARIMA Procedure
- 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: ARIMA Procedure
- References
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Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.