Partial likelihood inference for time series following generalized linear models
Date
2004Source
Journal of Time Series AnalysisVolume
25Issue
2Pages
173-197Google Scholar check
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The present article offers a certain unifying approach to time series regression modelling by combining partial likelihood (PL) inference and generalized linear models. An advantage gained by resorting to PL is that the joint distribution of the response and the covariates is left unspecified, and furthermore, PL allows for temporal or sequential conditional inference with respect to a filtration generated by all that is known to the observer at the time of observation. Two real data examples illustrate the methodology.