The PANEL Procedure
- Overview
- Getting Started
-
Syntax
-
DetailsSpecifying the Input DataSpecifying the Regression ModelUnbalanced DataMissing ValuesComputational ResourcesRestricted EstimatesNotationOne-Way Fixed-Effects ModelTwo-Way Fixed-Effects ModelBalanced PanelsUnbalanced PanelsFirst-Differenced Methods for One-Way and Two-Way ModelsBetween 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 Cross-Sectional Dependence TestPanel Data Unit Root TestsLagrange Multiplier (LM) Tests for Cross-Sectional and Time EffectsTests for Serial Correlation and Cross-Sectional EffectsTroubleshootingCreating ODS GraphicsOUTPUT OUT= Data SetOUTEST= Data SetOUTTRANS= Data SetPrinted OutputODS Table Names
-
Example
- References
Any observation in the input data set with a missing value for one or more of the regressors is ignored by PROC PANEL and is not used in the model fit.
If there are observations in the input data set with missing dependent variable values but with nonmissing regressors, PROC PANEL can compute predicted values and store them in an output data set by using the OUTPUT statement. Note that the presence of such observations with missing dependent variable values does not affect the model fit because these observations are excluded from the calculation.
If either some regressors or the dependent variable values are missing, the model is estimated as unbalanced where the number of time series observations across different cross sections does not have to be equal. The Parks and Da Silva methods cannot be used with unbalanced data.