**Tests of Homogeneity**

**chow <n>**where <n> is the number of regressions involved in the test. A Chow test is a special case of the Fisher

*F*test used to test the homogeneity of a sample.Typically, one runs two or more regressions covering parts of a sample and compares the sum of squared errors with that of a single regression covering the whole sample. If we were going to break up the sample into two parts, the command would be

chow 3

Then we run first the two regressions over the separate halves of the sample, and finally the single regression over the whole sample. After this last regression, one will be greeted by the results of the Chow test.

There is a special case in which the second “half” of the sample is too small to run the regression. In that case, give the command

*chow 2*and then run the regression over the reduced sample and then again over the whole sample. After the second regression, the Chow tests appear.

**Test of Normality of Errors**

**(norm)ality <y | n | f>**This command turns on (or off) tests of normality of the error terms. If the option chosen is ‘y’, then the Jarque-Bera test appears on the standard regression result screen labeled “JarqBer.” Under the hypothesis of normality, it has a chi-square distribution with two degrees of freedom.

If the option ‘f’ (for full) is chosen, then before the regression results are shown, a table appears with moments and measures of symmetry and peakedness, as well as the Jarque-Bera statistics. If a catch file is active, this table will go into it.