The AUTOREG Procedure

OUTEST= Data Set

The OUTEST= data set contains all the variables used in any MODEL statement. Each regressor variable contains the estimate for the corresponding regression parameter in the corresponding model. In addition, the OUTEST= data set contains the following variables:

_A_i: the ith order autoregressive parameter estimate. There are m such variables _A_1 through _A_m, where m is the value of the NLAG= option.
_AH_i: the ith order ARCH parameter estimate, if the GARCH= option is specified. There are q such variables _AH_1 through _AH_q, where q is the value of the Q= option. The variable _AH_0 contains the estimate of ${\omega }$ .
_AHP_i: the estimate of the ${\psi }_{i}$ parameter in the PGARCH model, if a PGARCH model is specified. There are q such variables _AHP_1 through _AHP_q, where q is the value of the Q= option.
_AHQ_i: the estimate of the ${\psi }_{i}$ parameter in the QGARCH model, if a QGARCH model is specified. There are q such variables _AHQ_1 through _AHQ_q, where q is the value of the Q= option.
_AHT_i: the estimate of the ${\psi }_{i}$ parameter in the TGARCH model, if a TGARCH model is specified. There are q such variables _AHT_1 through _AHT_q, where q is the value of the Q= option.
_DELTA_: the estimated mean parameter for the GARCH-M model if a GARCH-in-mean model is specified
_DEPVAR_: the name of the dependent variable
_GH_i: the ith order GARCH parameter estimate, if the GARCH= option is specified. There are p such variables _GH_1 through _GH_p, where p is the value of the P= option.
_HET_i: the ith heteroscedasticity model parameter specified by the HETERO statement
INTERCEPT: the intercept estimate. INTERCEPT contains a missing value for models for which the NOINT option is specified.
_METHOD_: the estimation method that is specified in the METHOD= option
_MODEL_: the label of the MODEL statement if one is given, or blank otherwise
_MSE_: the value of the mean square error for the model
_NAME_: the name of the row of covariance matrix for the parameter estimate, if the COVOUT option is specified
_LAMBDA_: the estimate of the power parameter ${\lambda }$ in the PGARCH model, if a PGARCH model is specified.
_LIKLHD_: the log-likelihood value of the GARCH model
_SSE_: the value of the error sum of squares
_START_: the estimated start-up value for the conditional variance when GARCH= (STARTUP=ESTIMATE) option is specified
_STATUS_: This variable indicates the optimization status. _STATUS $\_ =0$ indicates that there were no errors during the optimization and the algorithm converged. _STATUS $\_ =1$ indicates that the optimization could not improve the function value and means that the results should be interpreted with caution. _STATUS $\_ =2$ indicates that the optimization failed due to the number of iterations exceeding either the maximum default or the specified number of iterations or the number of function calls allowed. _STATUS $\_ =3$ indicates that an error occurred during the optimization process. For example, this error message is obtained when a function or its derivatives cannot be calculated at the initial values or during the iteration process, when an optimization step is outside of the feasible region or when active constraints are linearly dependent.
_STDERR_: standard error of the parameter estimate, if the COVOUT option is specified.
_TDFI_: the estimate of the inverted degrees of freedom for Student’s t distribution, if DIST=T is specified.
_THETA_: the estimate of the ${\theta }$ parameter in the EGARCH model, if an EGARCH model is specified.
_TYPE_: PARM for observations containing parameter estimates, or COV for observations containing covariance matrix elements.

The OUTEST= data set contains one observation for each MODEL statement giving the parameter estimates for that model. If the COVOUT option is specified, the OUTEST= data set includes additional observations for each MODEL statement giving the rows of the covariance of parameter estimates matrix. For covariance observations, the value of the _TYPE_ variable is COV, and the _NAME_ variable identifies the parameter associated with that row of the covariance matrix.