First-order observation-driven integer-valued autoregressive processes

A first-order observation-driven integer-valued autoregressive model is introduced. Ergodicity of the process is established. Conditional least squares and maximum likelihood estimators of the model parameters are derived. The performances of these estimators are compared via simulation. The models are applied to a real data set.

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Year of publication:	2008
Authors:	Zheng, Haitao ; Basawa, Ishwar V.
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 78.2008, 1, p. 1-9
Publisher:	Elsevier
Keywords:	Models of count data Thinning models Observation-driven model INAR models Random coefficient models Conditional least squares Maximum likelihood

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005211965