The PANEL Procedure
- Overview
- Getting Started
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Syntax
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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
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Example
- References
For fixed-effects models, random-effects models, between estimators, and dynamic panel estimators, the PANEL procedure can process data with different numbers of time series observations across different cross sections. The Parks and Da Silva methods cannot be used with unbalanced data. The missing time series observations are recognized by the absence of time series ID variable values in some of the cross sections in the input data set. Moreover, if an observation with a particular time series ID value and cross-sectional ID value is present in the input data set, but one or more of the model variables are missing, that time series point is treated as missing for that cross section.