This paper considers the problem of identification and estimation in panel-data sample-selection models
with a binary selection rule when the latent equations contain possibly predetermined variables, lags
of the dependent variables, and unobserved individual effects. The selection equation contains lags of
the dependent variables from both the latent and the selection equations as well as other possibly
predetermined variables relative to the latent equations. We derive a set of conditional moment restrictions
that are then exploited to construct a three-step sieve estimator for the parameters of the main equation
including a nonparametric estimator of the sample-selection term. In the second step the unknown parameters
of the selection equation are consistently estimated using a transformation approach in the spirit of
Berkson's minimum chi-square sieve method and a first-step kernel estimator for the selection probability.
This second-step estimator is of interest in its own right. It can be used to semiparametrically estimate a
panel-data binary response model with a nonparametric individual specific effect without making any other
distributional assumptions. We show that both estimators (second and third stage) are √n-consistent and
asymptotically normal.