A dynamic count process

Kim, Namhyun, Wongsa art, Pipat ORCID: https://orcid.org/0000-0002-7611-0383 and Xia, Yingcun 2024. A dynamic count process. Journal of Statistical Planning and Inference 233 , 106187. 10.1016/j.jspi.2024.106187 Item availability restricted.

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Abstract

The current paper aims to complement the recent development of the observation-driven models of dynamic counts with a parametric-driven one for a general case, particularly discrete two parameters exponential family distributions. The current paper proposes a finite semiparametric exponential mixture of SETAR processes of the conditional mean of counts to capture the nonlinearity and complexity. Because of the intrinsic latency of the conditional mean, the general additive state-space representation of dynamic counts is firstly proposed then stationarity and geometric ergodicity are established under a mild set of conditions. We also propose to estimate the unknown parameters by using quasi maximum likelihood estimation and establishes the asymptotic properties of the quasi maximum likelihood estimators (QMLEs), particularly T -consistency and normality under the relatively mild set of conditions. Furthermore, the finite sample properties of the QMLEs are investigated via simulation exercises and an illustration of the proposed process is presented by applying the proposed method to the intraday transaction counts per minute of AstraZeneca stock.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0378-3758
Date of First Compliant Deposit:	29 April 2024
Date of Acceptance:	18 April 2024
Last Modified:	09 Nov 2024 14:45
URI:	https://orca.cardiff.ac.uk/id/eprint/168475

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