The PANEL Procedure |

Specifying the Regression Model |

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, GMM and ITGMM(iterated GMM). See the section Details: PANEL Procedure 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 19.1 for more information.

In order to aid in model specification within this class of models, the procedure provides two specification test statistics. The first is an *F* statistic that tests the null hypothesis that the fixed-effects parameters are all zero. 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, but 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 zero. Rejection of the null hypothesis might suggest that the fixed-effects model is more appropriate.

The 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.

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