Residual and forecast methods in time series models with covariates

We are dealing with time series which are measured on an arbitrary
scale, e.g. on a categorical or ordinal scale, and which are
recorded together with time varying covariates. The conditional
expectations are modelled as a regression model, its parameters are
estimated via likelihood- or quasi-likelihood-approach. Our main
concern are diagnostic methods and forecasting procedures for such
time series models. Diagnostics are based on (partial) residual
measures as well as on (partial) residual variables; l-step
predictors are gained by an approximation formula for conditional
expectations. The various methods proposed are illustrated by two
different data sets.