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Periodic INAR(1) Models with Skellam-Distributed Innovations

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11621)

Abstract

In this paper, an integer-valued autoregressive model of order one (INAR(1)) with time-varying parameters and driven by a periodic sequence of innovations is introduced. The proposed INAR(1) model is based on the signed thinning operator defined by Kachour and Truquet (2011) and conveniently adapted to the periodic case. Basic notations and definitions concerning the periodic signed thinning operator are provided. Based on this thinning operator, Chesneau and Kachour (2012) established a signed INAR(1) model. Motivated by the work of Chesneau and Kachour (2012), we introduce a periodic model, denoted by S-PINAR(1), with period s. In contrast to conventional INAR(1) models, these models are defined in \(\mathbb {Z}\) allowing for negative values both for the series and its autocorrelation function. For a proper \(\mathbb {Z}\)-valued time series, a distribution for the innovation term defined on \(\mathbb {Z}\) is required. The S-PINAR(1) model assumes a specific innovation distribution, the Skellam distribution. Regarding parameter estimation, two methods are considered: conditional least squares and conditional maximum likelihood. The performance of the S-PINAR(1) model is assessed through a simulation study.

Keywords

  • Integer-valued autoregressive models
  • Signed thinning operator
  • Skellam distribution

Supported by Fundação para a Ciência e a Tecnologia (FCT), within projects UID/MAT/04106/2019 (CIDMA) and UID/Multi/04621/2019 (CEMAT/IST-ID).

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Correspondence to Cláudia Santos .

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Santos, C., Pereira, I., Scotto, M. (2019). Periodic INAR(1) Models with Skellam-Distributed Innovations. In: , et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-24302-9_6

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  • Publisher Name: Springer, Cham

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