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Partial likelihood inference for time series following generalized linear models

Research output: Contribution to journalJournal articlepeer-review

<mark>Journal publication date</mark>03/2004
<mark>Journal</mark>Journal of Time Series Analysis
Issue number2
Number of pages25
Pages (from-to)173-197
Publication StatusPublished
<mark>Original language</mark>English


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.