Inference for nonstationary time series of counts with application to change-point problems

William Kengne () and Isidore S. Ngongo ()
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William Kengne: CY Cergy Paris Université
Isidore S. Ngongo: Université de Yaoundé 1

Annals of the Institute of Statistical Mathematics, 2022, vol. 74, issue 4, No 10, 835 pages

Abstract: Abstract We consider an integer-valued time series $$(Y_t)_{t\in {\mathbb {Z}}}$$ ( Y t ) t ∈ Z where the model after a time $$k^*$$ k ∗ is Poisson autoregressive with the conditional mean that depends on a parameter $$\theta ^*\in \varTheta \subset {\mathbb {R}}^d$$ θ ∗ ∈ Θ ⊂ R d . The structure of the process before $$k^*$$ k ∗ is unknown; it could be any other integer-valued process, that is, $$(Y_t)_{t\in {\mathbb {Z}}}$$ ( Y t ) t ∈ Z could be nonstationary. It is established that the maximum likelihood estimator of $$\theta ^*$$ θ ∗ computed on the nonstationary observations is consistent and asymptotically normal. Subsequently, we carry out the sequential change-point detection in a large class of Poisson autoregressive models, and propose a monitoring scheme for detecting change. The procedure is based on an updated estimator, which is computed without the historical observations. The above results of inference in a nonstationary setting are applied to prove the consistency of the proposed procedure. A simulation study as well as a real data application are provided.

Keywords: Time series of counts; Poisson autoregression; likelihood estimation; Change-point; Sequential detection; Weak convergence (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10463-021-00815-1

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