The bayes prefix can fit Bayesian panel-data models. If you read Bayesian regression models using the bayes prefix, this may surprise you. But what you might have overlooked is that panel-data models can be fit using commands for multilevel models.
You can read all about Bayesian multilevel models.
But when you see
. mixed y x1 x2 || id:
. xtset id . xtreg y x1 x2
which fits a panel-data linear regression model with random intercepts by id. Thus, while you can't fit the Bayesian version of this model by typing
. bayes: xtreg y x1 x2
you can type
. bayes: mixed y x1 x2 || id:
And because you are using mixed, you are not limited to random intercepts. You can include random coefficients too. If the coefficient for x2 varies across ids, type
. bayes: mixed y x1 x2 || id: x2
For an example, see Random coefficients.
Bayesian panel-data models are not only for continuous outcomes. You can just as easily type for binary outcomes
. bayes: meprobit y x1 x2 || id:
for count outcomes
. bayes: mepoisson y x1 x2 || id:
or for censored outcomes
. bayes: metobit y x1 x2, ll(0) || id:
Or use any of the 12 multilevel estimators that support the bayes prefix.
For more information, see