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 each MODEL statement, the printed output from PROC PANEL includes the following:
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a model description, which gives the estimation method used, the model statement label if specified, the number of cross sections and the number of observations in each cross section, and the order of moving average error process for the DASILVA option. For fixed-effects model analysis, an F test for the absence of fixed effects is produced, and for random-effects model analysis, a Hausman test is used for the appropriateness of the random-effects specification.
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the estimates of the underlying error structure parameters
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the regression parameter estimates and analysis. For each regressor, this includes the name of the regressor, the degrees of freedom, the parameter estimate, the standard error of the estimate, a t statistic for testing whether the estimate is significantly different from 0, and the significance probability of the t statistic.
Optionally, PROC PANEL prints the following:
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the covariance and correlation of the resulting regression parameter estimates for each model and assumed error structure
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the
matrix that is the estimated contemporaneous covariance matrix for the PARKS option