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

Héctor Araya, Natalia Bahamonde, Tania Roa, Soledad Torres

Research output: Contribution to journalArticlepeer-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 languageEnglish
JournalCommunications in Statistics - Theory and Methods
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

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

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