Data-driven estimation of semiparametric fractional autoregressive models

Beran, Jan; Feng, Yuanhua

Data-driven estimation of semiparametric fractional autoregressive models

Dateien

499_1.pdfGröße: 365.94 KBDownloads: 89

Datum

2000

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Mathematik und Statistik

Publikationstyp

Working Paper/Technical Report

Publikationsstatus

Published

Zusammenfassung

In this paper data-driven algorithms for fitting SEMIFAR models (Beran, 1999) are proposed. The algorithms combine the data-driven estimation of the nonparametric trend and maximum likelihood estimation of the parameters. For selecting the bandwidth, the proposal of Beran and Feng (1999) based on the iterative plug-in idea (Gasser et al., 1991) is used. Asymptotic properties of the proposed algorithms are investigated. A large simulation study illustrates the practical performance of the methods.

Fachgebiet (DDC)

330 Wirtschaft

Schlagwörter

semiparametric models, long-range dependence, fractional ARIMA, antipersistence, nonparametric regression, bandwidth selection

Zitieren

ISO 690

BERAN, Jan, Yuanhua FENG, 2000. Data-driven estimation of semiparametric fractional autoregressive models

BibTex

@techreport{Beran2000Datad-594,
  year={2000},
  series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie},
  title={Data-driven estimation of semiparametric fractional autoregressive models},
  number={2000/16},
  author={Beran, Jan and Feng, Yuanhua}
}

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