  For discrete panel data, the dynamic relationship between successive
observations is often of interest. We consider a dynamic probit model for short
panel data. A problem with estimating the dynamic parameter of interest is that
the model contains a large number of nuisance parameters, one for each
individual. Heckman proposed to use maximum likelihood estimation of the
dynamic parameter, which, however, does not perform well if the individual
effects are large. We suggest new estimators for the dynamic parameter, based
on the assumption that the individual parameters are random and possibly large.
Theoretical properties of our estimators are derived and a simulation study
shows they have some advantages compared to Heckman's estimator.
