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 ModelHausman-Taylor EstimationAmemiya-MaCurdy EstimationParks Method (Autoregressive Model)Da Silva Method (Variance-Component Moving Average Model)Dynamic Panel EstimatorsLinear 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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Examples
- References
Computational Resources
The more parameters there are to be estimated, the more memory and time are required to estimate the model. Also affecting
these resources are the estimation method chosen and the method to calculate variance components. If the model has p parameters including the intercept, there are at least 
numbers being held in the memory.
If the Arellano and Bond GMM approach is used, the amount of memory grows proportionately to the number of instruments in the INSTRUMENT statement. If the ITGMM (iterated GMM) option is selected, the computation time also depends on the convergence criteria selected and the maximum number of iterations allowed.