The HPPANEL Procedure

The PERFORMANCE statement in PROC HPPANEL is specified like the PERFORMANCE statement in other SAS high-performance procedures. The following statements execute the model in the distributed computing environment with two threads and four nodes:

   proc hppanel data=a;
      id state date;
      model y = x1 x2;
      performance nodes=2 nthreads=4;
   run;

The major advantage of using PROC HPPANEL is that you can incorporate a model for the structure of the random errors. It is important to consider what type 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 HPPANEL procedure are FIXONE, FIXONETIME, FIXTWO, RANONE, and RANTWO. For more information about these methods and the error structures they assume, see the following sections. The following statements fit a Fuller-Battese one-way random-effects model:

   proc hppanel data=a;
      id state date;
      model y = x1 x2 / ranone vcomp=fb;
      performance nodes=0 nthreads=1;
   run;

To aid in model specification within this class of models, PROC HPPANEL provides one specification test statistic, the Hausman m statistic, which 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, ordinary least squares (OLS) and generalized least squares (GLS) are consistent. However, OLS is inefficient. Hence, a test can be based on the result that the covariance between an efficient estimator and its difference from an inefficient estimator is 0. Rejection of the null hypothesis might suggest that the fixed-effects model is more appropriate.

The HPPANEL 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. For OLS estimation, the Buse R-square measure is equivalent to the usual R-square measure.