The ARIMA Procedure
- Overview
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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
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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
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Examples
- References
Getting Started: ARIMA Procedure
This section outlines the use of the ARIMA procedure and gives a cursory description of the ARIMA modeling process for readers who are less familiar with these methods.
- 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