Prediction and classification of non-stationary categorical time series

Partial likelihood analysis of a general regression model for the analysis of nonstationary categorical time series is presented, taking into account stochastic time dependent covariates. The model links the probabilities of each category to a covariate process through a vector of time invariant par ameters. Under mild regularity conditions, we establish good asymptotic pro perties of the estimator by appealing to martingale theory. Certain diagnos tic tools are presented for checking the adequacy of the fit. (C) 1998 Acad emic Press.