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).
The parameters of the model are estimated.
A second linear regression is performed on the residuals, , from the nonlinear estimation in step one.

where is Jacobian columns that are evaluated at the parameter estimates. If the estimation is an instrumental variables estimation with matrix of instruments , then the following regression is performed:

where is the projection matrix.
The restricted SSE (RSSE) from this regression is obtained. An SSE for each subsample is then obtained by using the same linear regression.
The F statistic is then

This test has and degrees of freedom.
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 number of observations in the first sample or a parenthesized list of first sample sizes. 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 effects the intercept parameter). The output from the requested Chow tests are shown in Figure 19.57.
Figure 19.57: Chow’s Test Results
Structural Change Test  

Test  Break Point  Num DF  Den DF  F Value  Pr > F 
Chow  40  2  96  12.95  <.0001 
Chow  50  2  96  101.37  <.0001 
Chow  60  2  96  26.43  <.0001 
Predictive Chow  90  11  87  1.86  0.0566 