  Point and interval estimation of future disability inception and recovery
rates are predominantly carried out by combining generalized linear models
(GLM) with time series forecasting techniques into a two-step method involving
parameter estimation from historical data and subsequent calibration of a time
series model. This approach may in fact lead to both conceptual and numerical
problems since any time trend components of the model are incoherently treated
as both model parameters and realizations of a stochastic process. We suggest
that this general two-step approach can be improved in the following way:
First, we assume a stochastic process form for the time trend component. The
corresponding transition densities are then incorporated into the likelihood,
and the model parameters are estimated using the Expectation-Maximization
algorithm. We illustrate the modelling procedure by fitting the model to
Swedish disability claims data.
