Efficient estimation of autoregression parameters and innovation distributions for semiparametric integer-valued AR(p) models

Research output: Contribution to journalArticleScientificpeer-review

45 Citations (Scopus)

Abstract

Integer-valued auto-regressive (INAR) processes have been introduced to model non-negative integer-valued phenomena that evolve over time. The distribution of an INAR(p) process is essentially described by two parameters:  a vector of auto-regression coefficients and a probability distribution on the non-negative integers, called an immigration or innovation distribution. Traditionally, parametric models are considered where the innovation distribution is assumed to belong to a parametric family. The paper instead considers a more realistic semiparametric INAR(p) model where there are essentially no restrictions on the innovation distribution. We provide an (semiparametrically) efficient estimator of both the auto-regression parameters and the innovation distribution. 
Original languageEnglish
Pages (from-to)467-485
JournalJournal of the Royal Statistical Society, Series B
Volume71
Issue number2
Publication statusPublished - 2009

Fingerprint Dive into the research topics of 'Efficient estimation of autoregression parameters and innovation distributions for semiparametric integer-valued AR(p) models'. Together they form a unique fingerprint.

  • Cite this