  The detection of serial dependence in binary or binomial valued time series
is difficult using standard time series methods, particularly when there are
regression effects to be modelled. In this paper we derive score-type tests for
detecting departures from independence in the directions of the GLARMA\ and
BARMA\ type observation driven models. These score tests can easily be applied
using a standard logistic regression and so may have appeal to practitioners
who wish to initially assess the need to incorporate serial dependence effects.
To deal with the nuisance parameters in some GLARMA models a supremum type test
is implemented.
