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

Research output: Contribution to journal › Article › peer-review

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
Journal	Communications in Statistics - Theory and Methods
DOIs	https://doi.org/10.1080/03610926.2021.1973504
State	Accepted/In press - 2021
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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title = "Parameter estimation for a discrete time model driven by fractional Poisson process",

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.",

keywords = "62F10, 62F12, 62M09, Fractional Poisson process, long memory, maximum likelihood estimator, weighted least square estimator",

author = "H{\'e}ctor Araya and Natalia Bahamonde and Tania Roa and Soledad Torres",

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year = "2021",

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language = "English",

journal = "Communications in Statistics - Theory and Methods",

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AU - Araya, Héctor

AU - Bahamonde, Natalia

AU - Roa, Tania

AU - Torres, Soledad

PY - 2021

Y1 - 2021

N2 - 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.

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.

KW - 62F10

KW - 62F12

KW - 62M09

KW - Fractional Poisson process

KW - long memory

KW - maximum likelihood estimator

KW - weighted least square estimator

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U2 - 10.1080/03610926.2021.1973504

DO - 10.1080/03610926.2021.1973504

M3 - Article

AN - SCOPUS:85114645609

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

ER -

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

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Keywords

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