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
The following statements use the cost function data from Greene (1990) to estimate the variance components model. The variable PRODUCTION is the log of output in millions of kilowatt-hours, and COST is the log of cost in millions of dollars. See Greene (1990) for details.
data greene; input firm year production cost @@; datalines; 1 1955 5.36598 1.14867 1 1960 6.03787 1.45185 1 1965 6.37673 1.52257 1 1970 6.93245 1.76627 2 1955 6.54535 1.35041 2 1960 6.69827 1.71109 2 1965 7.40245 2.09519 2 1970 7.82644 2.39480 3 1955 8.07153 2.94628 3 1960 8.47679 3.25967 ... more lines ...
You decide to fit the following model to the data:
![\[ C_{it}= \mr{Intercept} + {\bbeta }P_{it}+v_{i}+e_{t}+{\epsilon }_{it} \; \; i=1, {\ldots }, \mi{N} ; \; t=1, {\ldots }, \mi{T} \]](images/etsug_panel0001.png){kind=small}
where 
and 
represent the cost and production, and 
, 
and 
are the cross-sectional, time series, and error variance components.
If you assume that the time and cross-sectional effects are random, you are left with four possible estimators for the variance components. You choose Fuller-Battese.
The following statements fit this model.
proc sort data=greene; by firm year; run; proc panel data=greene; model cost = production / rantwo vcomp = fb; id firm year; run;
The PANEL procedure output is shown in Figure 20.1. A model description is printed first, which reports the estimation method used and the number of cross sections and time periods. The variance components estimates are printed next. Finally, the table of regression parameter estimates shows the estimates, standard errors, and t tests.