
COVB

prints the inverse of the observed information matrix for the parameter estimates. This matrix is an estimate of the covariance
matrix for the parameter estimates.

DETTOL=value

specifies the convergence criterion. The DETTOL= and PARMTOL= option values are used together to test for convergence of the
estimation process. If, during an iteration, the relative change of the parameter estimates is less than the PARMTOL= value
and the relative change of the determinant of the innovation variance matrix is less than the DETTOL= value, then iteration
ceases and the current estimates are accepted. The default is DETTOL=1E–5.

ITPRINT

prints the iterations during the estimation process.

KLAG=n

sets an upper limit for the number of lags of the sample autocovariance matrix used in computing the approximate likelihood
function. If the data have a strong moving average character, a larger KLAG= value might be necessary to obtain good estimates.
The default is KLAG=15. See the section Parameter Estimation for details.

MAXIT=n

sets an upper limit to the number of iterations in the maximum likelihood or conditional least squares estimation. The default
is MAXIT=50.

NOEST

suppresses the final maximum likelihood estimation of the selected model.

OUTMODEL=SASdataset

writes the parameter estimates and their standard errors to a SAS data set. See the section OUTMODEL= Data Set for details.

PARMTOL=value

specifies the convergence criterion. The DETTOL= and PARMTOL= option values are used together to test for convergence of the
estimation process. If, during an iteration, the relative change of the parameter estimates is less than the PARMTOL= value
and the relative change of the determinant of the innovation variance matrix is less than the DETTOL= value, then iteration
ceases and the current estimates are accepted. The default is PARMTOL=0.001.

RESIDEST

computes the final estimates by using conditional least squares on the raw data. This type of estimation might be more stable
than the default maximum likelihood method but is usually more computationally expensive. See the section Parameter Estimation for details about the conditional least squares method.

SINGULAR=value

specifies the criterion for testing for singularity of a matrix. A matrix is declared singular if a scaled pivot is less than
the SINGULAR= value when sweeping the matrix. The default is SINGULAR=1E–7.