OUTEST= Data Set

The SYSLIN Procedure

OUTEST= Data Set

The OUTEST= option produces a TYPE=EST output SAS data set containing estimates from the regressions. The variables in the OUTEST= data set are as follows:

BY variables: the BY statement variables are included in the OUTEST= data set
_TYPE_: identifies the estimation type for the observations. The _TYPE_ value INST indicates first-stage regression estimates. Other values indicate the estimation method used: 2SLS indicates two-stage least squares results, 3SLS indicates three-stage least squares results, LIML indicates limited information maximum likelihood results, and so forth. Observations added by IDENTITY statements have the _TYPE_ value IDENTITY.
_MODEL_: the model label. The model label is the label specified on the MODEL statement or the dependent variable name if no label is specified. For first-stage regression estimates, _MODEL_ has the value FIRST.
_DEPVAR_: the name of the dependent variable for the model
_NAME_: the names of the regressors for the rows of the covariance matrix, if the COVOUT option is specified. _NAME_ has a blank value for the parameter estimates observations. The _NAME_ variable is not included in the OUTEST= data set unless the COVOUT option is used to output the covariance of parameter estimates matrix.
_SIGMA_: contains the root mean square error for the model, which is an estimate of the standard deviation of the error term. The _SIGMA_ variable contains the same values reported as Root MSE in the printed output.
INTERCEPT: the intercept parameter estimates
regressors: the regressor variables from all the MODEL statements are included in the OUTEST= data set. Variables used in IDENTIFY statements are also included in the OUTEST= data set.

The parameter estimates are stored under the names of the regressor variables. The intercept parameters are stored in the variable INTERCEP. The dependent variable of the model is given a coefficient of -1. Variables not in a model have missing values for the OUTEST= observations for that model.

Some estimation methods require computation of preliminary estimates. All estimates computed are output to the OUTEST= data set. For each BY group and each estimation, the OUTEST= data set contains one observation for each MODEL or IDENTITY statement. Results for different estimations are identified by the _TYPE_ variable.

For example, consider the following statements:


   proc syslin data=a outest=est 3sls;
      by b;
      endogenous y1 y2;
      instruments x1-x4;
      model y1 = y2 x1 x2;
      model y2 = y1 x3 x4;
      identity x1 = x3 + x4;
   run;

The 3SLS method requires both a preliminary 2SLS stage and preliminary first stage regressions for the endogenous variable. The OUTEST= data set thus contains 3 different kinds of estimates. The observations for the first-stage regression estimates have the _TYPE_ value INST. The observations for the 2SLS estimates have the _TYPE_ value 2SLS. The observations for the final 3SLS estimates have the _TYPE_ value 3SLS.

Since there are 2 endogenous variables in this example, there are 2 first-stage regressions and 2 _TYPE_=INST observations in the OUTEST= data set. Since there are 2 model statements, there are 2 OUTEST= observations with _TYPE_=2SLS and 2 observations with _TYPE_=3SLS. In addition, the OUTEST= data set contains an observation with the _TYPE_ value IDENTITY containing the coefficients specified by the IDENTITY statement. All these observations are repeated for each BY-group in the input data set defined by the values of the BY variable B.

When the COVOUT option is specified, the estimated covariance matrix for the parameter estimates is included in the OUTEST= data set. Each observation for parameter estimates is followed by observations containing the rows of the parameter covariance matrix for that model. The row of the covariance matrix is identified by the variable _NAME_. For observations that contain parameter estimates, _NAME_ is blank. For covariance observations, _NAME_ contains the regressor name for the row of the covariance matrix, and the regressor variables contain the covariances.

See Example 19.1 for an example of the OUTEST= data set.

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