The PANEL Procedure
- Overview
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Getting Started
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Syntax
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DetailsMissing ValuesComputational ResourcesRestricted EstimatesNotationOne-Way Fixed-Effects ModelTwo-Way Fixed-Effects ModelBalanced PanelsUnbalanced PanelsBetween EstimatorsPooled EstimatorOne-Way Random-Effects ModelTwo-Way Random-Effects ModelParks Method (Autoregressive Model)Da Silva Method (Variance-Component Moving Average Model)Dynamic Panel EstimatorLinear Hypothesis TestingHeteroscedasticity-Corrected Covariance MatricesHeteroscedasticity- and Autocorrelation-Consistent Covariance MatricesR SquareSpecification TestsPanel Data Poolability TestPanel Data Unit Root TestsTroubleshootingCreating ODS GraphicsOUTPUT OUT= Data SetOUTEST= Data SetOUTTRANS= Data SetPrinted OutputODS Table Names
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Example
- References
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_
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is a character variable that contains the label for the MODEL statement if a label is specified.
- _METHOD_
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is a character variable that identifies the estimation method.
- _TYPE_
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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_
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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_
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is a character variable that contains the name of the response variable.
- _MSE_
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is the mean square error of the transformed model.
- _CSID_
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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_
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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_
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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_
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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
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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
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is the intercept parameter estimate. (Intercept is missing for models when the NOINT option is specified.)
- regressors
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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.