The ARIMA Procedure |

OUTSTAT= Data Set |

PROC ARIMA writes the diagnostic statistics for a model to an output data set when the OUTSTAT= option is specified in the ESTIMATE statement. The OUTSTAT 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).

_TYPE_, a character variable that contains the estimation method used. _TYPE_ can have the value CLS, ULS, or ML.

_STAT_, a character variable that contains the name of the statistic given by the _VALUE_ variable in this observation. _STAT_ takes on the values AIC, SBC, LOGLIK, SSE, NUMRESID, NPARMS, NDIFS, ERRORVAR, MU, CONV, and NITER.

_VALUE_, a numeric variable that contains the value of the statistic named by the _STAT_ variable.

The observations contained in the OUTSTAT= data set are identified by the _STAT_ variable. A description of the values of the _STAT_ variable follows:

- AIC
Akaike’s information criterion

- SBC
Schwarz’s Bayesian criterion

- LOGLIK
the log-likelihood, if METHOD=ML or METHOD=ULS is specified

- SSE
the sum of the squared residuals

- NUMRESID
the number of residuals

- NPARMS
the number of parameters in the model

- NDIFS
the sum of the differencing lags employed for the response variable

- ERRORVAR
the estimate of the innovation variance

- MU
the estimate of the mean term

- CONV
tells if the estimation converged. The value of 0 signifies that estimation converged. Nonzero values reflect convergence problems.

- NITER
the number of iterations

**Remark**. CONV takes an integer value that corresponds to the error condition of the parameter estimation process. The value of 0 signifies that estimation process has converged. The higher values signify convergence problems of increasing severity. Specifically:

CONV indicates that the estimation process has converged.

CONV or indicates that the estimation process has run into numerical problems (such as encountering an unstable model or a ridge) during the iterations.

CONV indicates that the estimation process has failed to converge.

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