What’s this about?

Suppose you have panel data and want to fit a random-effects model to an interval-measured outcome such as income bracket or age group.

If ylower and yupper record the upper and lower endpoints of the outcome, we could type

  . xtset id
  . xtintreg ylower yupper x1 x2 x3

to fit a model with random intercepts by id. The xtintreg command is not new, but the meintreg command is. You can fit the same model with meintreg by typing

  . meintreg ylower yupper x1 x2 x3 || id:

The advantage of using meintreg is that it does not restrict us to random intercepts. What if the coefficient for x1 varies across the levels of id? We can fit a random-coefficients model by typing

  . meintreg ylower yupper x1 x2 x3 || id: x1

You can see an example and learn more about the new meintreg command. That example fits a model with city-level random intercepts,

  . meintreg exerlo exerup age work kids walk || cid:

  . meintreg exerlo exerup age work kids walk || cid: age work

Tell me more

You can also fit Bayesian panel-data (multilevel) interval regression using the bayes prefix.

Learn more about Stata’s panel-data features.

Read more about multilevel interval regression in the Stata Multilevel Mixed-Effects Reference Manual; see [ME] meintreg.