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
-
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
- References
| OUTMODEL= SAS Data Set |
The OUTMODEL= option in the ESTIMATE statement writes an output data set that enables you to reconstruct the model. The OUTMODEL= data set contains much the same information as the OUTEST= data set but in a transposed form that might be more useful for some purposes. In addition, the OUTMODEL= data set includes the differencing operators.
The OUTMODEL data set contains the following:
the BY variables
_MODLABEL_, a character variable that contains the model label, if it is provided by using the label option in the ESTIMATE statement (otherwise this variable is not created).
_NAME_, a character variable that contains the name of the response or input variable for the observation.
_TYPE_, a character variable that contains the estimation method that was employed. The value of _TYPE_ can be CLS, ULS, or ML.
_STATUS_, a character variable that describes the convergence status of the model. A value of 0_CONVERGED indicates that the model converged.
_PARM_, a character variable that contains the name of the parameter given by the observation. _PARM_ takes on the values ERRORVAR, MU, AR, MA, NUM, DEN, and DIF.
_VALUE_, a numeric variable that contains the value of the estimate defined by the _PARM_ variable.
_STD_, a numeric variable that contains the standard error of the estimate.
_FACTOR_, a numeric variable that indicates the number of the factor to which the parameter belongs.
_LAG_, a numeric variable that contains the number of the term within the factor that contains the parameter.
_SHIFT_, a numeric variable that contains the shift value for the input variable associated with the current parameter.
The values of _FACTOR_ and _LAG_ identify which particular MA, AR, NUM, or DEN parameter estimate is given by the _VALUE_ variable. The _NAME_ variable contains the response variable name for the MU, AR, or MA parameters. Otherwise, _NAME_ contains the input variable name associated with NUM or DEN parameter estimates. The _NAME_ variable contains the appropriate variable name associated with the current DIF observation as well. The _VALUE_ variable is 1 for all DIF observations, and the _LAG_ variable indicates the degree of differencing employed.
The observations contained in the OUTMODEL= data set are identified by the _PARM_ variable. A description of the values of the _PARM_ variable follows:
- NUMRESID
_VALUE_ contains the number of residuals.
- NPARMS
_VALUE_ contains the number of parameters in the model.
- NDIFS
_VALUE_ contains the sum of the differencing lags employed for the response variable.
- ERRORVAR
_VALUE_ contains the estimate of the innovation variance.
- MU
_VALUE_ contains the estimate of the mean term.
- AR
_VALUE_ contains the estimate of the autoregressive parameter indexed by the _FACTOR_ and _LAG_ variable values.
- MA
_VALUE_ contains the estimate of a moving-average parameter indexed by the _FACTOR_ and _LAG_ variable values.
- NUM
_VALUE_ contains the estimate of the parameter in the numerator factor of the transfer function of the input variable indexed by the _FACTOR_, _LAG_, and _SHIFT_ variable values.
- DEN
_VALUE_ contains the estimate of the parameter in the denominator factor of the transfer function of the input variable indexed by the _FACTOR_, _LAG_, and _SHIFT_ variable values.
- DIF
_VALUE_ contains the difference operator defined by the difference lag given by the value in the _LAG_ variable.