Parameter estimation for a discrete time model driven by fractional Poisson process

Héctor Araya; Natalia Bahamonde; Tania Roa; Soledad Torres

doi:10.1080/03610926.2021.1973504

Parameter estimation for a discrete time model driven by fractional Poisson process

Héctor Araya, Natalia Bahamonde, Tania Roa, Soledad Torres

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1 Scopus citations

Abstract

In this article, we study the parametric problem of estimating the coefficient for a discrete time model driven by a fractional Poisson noise, when high-frequency observations are given. We consider weighted least squares and maximum likelihood estimators. Thus, asymptotic behavior of the estimators is proved and a simulation study is shown to illustrate our results.

Original language	English
Pages (from-to)	3452-3477
Number of pages	26
Journal	Communications in Statistics - Theory and Methods
Volume	52
Issue number	10
DOIs	https://doi.org/10.1080/03610926.2021.1973504
State	Published - 2023
Externally published	Yes

Keywords

62F10
62F12
62M09
Fractional Poisson process
long memory
maximum likelihood estimator
weighted least square estimator

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10.1080/03610926.2021.1973504

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AU - Bahamonde, Natalia

AU - Roa, Tania

AU - Torres, Soledad

PY - 2023

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AB - In this article, we study the parametric problem of estimating the coefficient for a discrete time model driven by a fractional Poisson noise, when high-frequency observations are given. We consider weighted least squares and maximum likelihood estimators. Thus, asymptotic behavior of the estimators is proved and a simulation study is shown to illustrate our results.

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KW - 62M09

KW - Fractional Poisson process

KW - long memory

KW - maximum likelihood estimator

KW - weighted least square estimator

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Parameter estimation for a discrete time model driven by fractional Poisson process

Abstract

Keywords

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