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 PanelsBetween 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 Unit Root TestsTroubleshootingCreating ODS GraphicsOUTPUT OUT= Data SetOUTEST= Data SetOUTTRANS= Data SetPrinted OutputODS Table Names
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Example
- References
The MODEL statement in PROC PANEL is specified like the MODEL statement in other SAS regression procedures: the dependent variable is listed first, followed by an equal sign, followed by the list of regressor variables, as shown in the following statements:
proc panel data=a;
id state date;
model y = x1 x2;
run;
The major advantage of using PROC PANEL is that you can incorporate a model for the structure of the random errors. It is important to consider what kind of error structure model is appropriate for your data and to specify the corresponding option in the MODEL statement.
The error structure options supported by the PANEL procedure are FIXONE, FIXONETIME, FIXTWO, RANONE, RANTWO, PARKS, DASILVA, GMM1, GMM2, and ITGMM (iterated GMM). See the following sections for more information about these methods and the error structures they assume. The following statements fit a Fuller-Battese one-way random-effects model:
proc panel data=a;
id state date;
model y = x1 x2 / ranone vcomp=fb;
run;
You can specify more than one error structure option in the MODEL statement; the analysis is repeated using each specified method. You can use any number of MODEL statements to estimate different regression models or estimate the same model by using different options. See Example 20.1 for more information.
To aid in model specification within this class of models, PROC PANEL provides two specification test statistics. The first is an F statistic that tests the null hypothesis that the fixed-effects parameters are all 0. The second is a Hausman m statistic that provides information about the appropriateness of the random-effects specification. The m statistic is based on the idea that, under the null hypothesis of no correlation between the effects variables and the regressors, OLS and GLS are consistent. However, OLS is inefficient. Hence, a test can be based on the result that the covariance of an efficient estimator with its difference from an inefficient estimator is 0. Rejection of the null hypothesis might suggest that the fixed-effects model is more appropriate.
The PANEL procedure also provides the Buse R-square measure. This number is interpreted as a measure of the proportion of the transformed sum of squares of the dependent variable that is attributable to the influence of the independent variables. In the case of OLS estimation, the Buse R-square measure is equivalent to the usual R-square measure.