  Variables such as household income are sometimes binned, so that we only know
how many households fall in each of several bins such as $0-10,000,
$10,000-15,000, or $200,000+. We provide a SAS macro that estimates the mean
and variance of binned data by fitting the extended generalized gamma (EGG)
distribution, the power normal (PN) distribution, and a new distribution that
we call the power logistic (PL). The macro also implements a "best-of-breed"
estimator that chooses from among the EGG, PN, and PL estimates on the basis of
likelihood and finite variance. We test the macro by estimating the mean family
and household incomes of approximately 13,000 US school districts between 1970
and 2009. The estimates have negligible bias (0-2%) and a root mean squared
error of just 3-6%. The estimates compare favorably with estimates obtained by
fitting the Dagum, generalized beta (GB2), or logspline distributions.
