The PANEL Procedure
- Overview
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Getting Started
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Syntax
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Details
Missing Values Computational Resources Restricted Estimates Notation The One-Way Fixed-Effects Model The Two-Way Fixed-Effects Model Balanced Panels Unbalanced Panels Between Estimators Pooled Estimator The One-Way Random-Effects Model The Two-Way Random-Effects Model Parks Method (Autoregressive Model) Da Silva Method (Variance-Component Moving Average Model) Dynamic Panel Estimator Linear Hypothesis Testing Heteroscedasticity-Corrected Covariance Matrices R Square Specification Tests Troubleshooting ODS Graphics The OUTPUT OUT= Data Set The OUTEST= Data Set The OUTTRANS= Data Set Printed Output ODS Table Names
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Examples
- References
| The OUTEST= Data Set |
PROC PANEL writes the parameter estimates to an output data set when the OUTEST= option is specified. The OUTEST= data set contains the following variables:
- _MODEL_
is a character variable that contains the label for the MODEL statement if a label is specified.
- _METHOD_
is a character variable that identifies the estimation method.
- _TYPE_
is a character variable that identifies the type of observation. Values of the _TYPE_ variable are CORRB, COVB, CSPARMS, STD, and the type of model estimated. The CORRB observation contains correlations of the parameter estimates, the COVB observation contains covariances of the parameter estimates, the CSPARMS observation contains cross-sectional parameter estimates, the STD observation indicates the row of standard deviations of the corresponding coefficients, and the type of model estimated observation contains the parameter estimates.
- _NAME_
is a character variable that contains the name of a regressor variable for COVB and CORRB observations and is left blank for other observations. The _NAME_ variable is used in conjunction with the _TYPE_ values COVB and CORRB to identify rows of the correlation or covariance matrix.
- _DEPVAR_
is a character variable that contains the name of the response variable.
- _MSE_
is the mean square error of the transformed model.
- _CSID_
is the value of the cross section ID for CSPARMS observations. The _CSID_ variable is used with the _TYPE_ value CSPARMS to identify the cross section for the first-order autoregressive parameter estimate contained in the observation. The _CSID_ variable is missing for observations with other _TYPE_ values. (Currently, only the _A_1 variable contains values for CSPARMS observations.)
- _VARCS_
is the variance component estimate due to cross sections. The _VARCS_ variable is included in the OUTEST= data set when either the FULLER or DASILVA option is specified.
- _VARTS_
is the variance component estimate due to time series. The _VARTS_ variable is included in the OUTEST= data set when either the FULLER or DASILVA option is specified.
- _VARERR_
is the variance component estimate due to error. The _VARERR_ variable is included in the OUTEST= data set when the FULLER option is specified.
- _A_1
is the first-order autoregressive parameter estimate. The _A_1 variable is included in the OUTEST= data set when the PARKS option is specified. The values of _A_1 are cross-sectional parameters, meaning that they are estimated for each cross section separately. The _A_1 variable has a value only for _TYPE_=CSPARMS observations. The cross section to which the estimate belongs is indicated by the _CSID_ variable.
- Intercept
is the intercept parameter estimate. (Intercept is missing for models when the NOINT option is specified.)
- regressors
are the regressor variables specified in the MODEL statement. The regressor variables in the OUTEST= data set contain the corresponding parameter estimates for the model identified by _MODEL_ for _TYPE_=PARMS observations, and the corresponding covariance or correlation matrix elements for _TYPE_=COVB and _TYPE_=CORRB observations. The response variable contains the value–1 for the _TYPE_=PARMS observation for its model.