The MODEL Procedure

The Chow test is used to test for break points or structural changes in a model. The problem is posed as a partitioning of the data into two parts of size and . The null hypothesis to be tested is

where is estimated by using the first part of the data and is estimated by using the second part.

The test is performed as follows (see Davidson and MacKinnon 1993, p. 380):

Chow’s test is not applicable if , since one of the two subsamples does not contain enough data to estimate . In this instance, the predictive Chow test can be used. The predictive Chow test is defined as

where . This test can be derived from the Chow test by noting that the when and by adjusting the degrees of freedom appropriately.

You can select the Chow test and the predictive Chow test by specifying the CHOW=arg and the PCHOW=arg options in the FIT statement, where arg is either the first observation in the second sample or a parenthesized list of first observations in each of the second samples. If the size of the one of the two groups in which the sample is partitioned is less than the number of parameters, then a predictive Chow test is automatically used. These tests statistics are not produced for GMM and FIML estimations.

The following is an example of the use of the Chow test:

data exp;
   x=0;
   do time=1 to 100;
      if time=50 then x=1;
      y = 35 * exp( 0.01 * time ) + rannor( 123 ) + x * 5;
      output;
   end;
run;

proc model data=exp;
   parm zo 35 b;
      dert.z = b * z;
      y=z;
   fit y init=(z=zo) / chow =(40 50 60) pchow=90;
run;

The data set introduces an artificial structural change into the model (the structural change affects the intercept parameter). The output from the requested Chow tests are shown in Figure 57.