The ARIMA Procedure |

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.

Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.