ARCHIVED: In Stata, how do I conduct the Chow Test?
The Chow Test examines whether parameters (slopes and the intercept) of one group are different from those of other groups. If you are interested only in differences among intercepts, try a dummy variable regression model (fixed-effect model).
Suppose you suspect that the impact of salary
on
employees' motivation
varies across companies; the slope
of salary
of one company is different from the slopes of
other companies. For the sake of convenience, consider only two
companies (d
=1 or 0) here; size
and
culture
are covariates.
The pooled model, which assumes both companies have the same slopes and intercept, is as follows:
. regress motivation salary size culture
You may fit separate regressions as follows:
. regress motivation salary size culture if d==1 // for company 1 . regress motivation salary size culture if d==0 // for company 2
For the Chow Test, create an interaction term of the regressor
salary
and the dummy variable d
, and then
fit the model with the interaction and the dummy as follows:
. gen salary_d = salary * d . regress motivation salary salary_d d size culture
The coefficient of d
is the deviation of the second
company's intercept from the baseline intercept
(d
=0). Likewise, the coefficient of salary
is the slope of the baseline company, and the coefficient of
salary_d
is the deviation of the comparison group's slope
from the baseline slope.
Now, conduct the Chow Test using the .test
command. The null hypothesis is that two companies have equal
parameters for salary
and intercept; deviations of the
slope and intercept are not statistically discernible from zero.
. test _b[salary_d]=0, notest . test _b[d]=0, accum
The notest
option suppresses the output, and
accum
tests a hypothesis jointly with a previously tested
one. Rejection of the null hypothesis means that two companies do not
share the same intercept and slope of salary
.
For more details about the Chow Test, see Stata's Chow tests FAQ.
If you have questions about using statistical and mathematical software at Indiana University, contact the UITS Research Applications and Deep Learning team.
This is document chow in the Knowledge Base.
Last modified on 2023-05-09 14:39:42.