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

INSTITUTE OF STATISTICS

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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

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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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.",

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AU - Roa, Tania

AU - Torres, Soledad

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KW - 62M09

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

Abstract

Keywords

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