Maximum Likelihood Estimation for an Observation Driven Model for Poisson Counts

Richard A. Davis (), William T. M. Dunsmuir () and Sarah B. Streett ()
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Richard A. Davis: Colorado State University
William T. M. Dunsmuir: University of New South Wales
Sarah B. Streett: National Institute of Standards and Technology

Methodology and Computing in Applied Probability, 2005, vol. 7, issue 2, 149-159

Abstract: Abstract This paper is concerned with an observation-driven model for time series of counts whose conditional distribution given past observations follows a Poisson distribution.This class of models is capable of modeling a wide range of dependence structures and is readily estimated using an approximation to the likelihood function. Recursive formulae for carrying out maximum likelihood estimation are provided and the technical components required for establishing a central limit theorem of the maximum likelihood estimates are given in a special case.

Keywords: observation-driven model; Poisson valued time series (search for similar items in EconPapers)
Date: 2005
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DOI: 10.1007/s11009-005-1480-4

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