In this paper we present a review of some well-known bootstrap methods for time series data. We concentrate on block bootstrap and sieve bootstrap, whose validity has been proved to be extended to stationary long memory time series. We will start by reviewing briefly the peculiar features of the bootstrap methods and the issues raised in case of long range dependent data; then we present a Monte Carlo experiment to compare the performance of the methods for a variety of ARFIMA processes. Comments about the finite sample performance of the methods will be provided also in light of the established theoretical properties of the methods

Bootstrap methods for long-range dependence Monte Carlo evidence

Margherita Gerolimetto;Stefano Magrini
2020-01-01

Abstract

In this paper we present a review of some well-known bootstrap methods for time series data. We concentrate on block bootstrap and sieve bootstrap, whose validity has been proved to be extended to stationary long memory time series. We will start by reviewing briefly the peculiar features of the bootstrap methods and the issues raised in case of long range dependent data; then we present a Monte Carlo experiment to compare the performance of the methods for a variety of ARFIMA processes. Comments about the finite sample performance of the methods will be provided also in light of the established theoretical properties of the methods
2020
LXXIV
File in questo prodotto:
File Dimensione Formato  
Gerolimetto&Magrini_SIEDS2020.pdf

accesso aperto

Tipologia: Versione dell'editore
Licenza: Accesso libero (no vincoli)
Dimensione 534.8 kB
Formato Adobe PDF
534.8 kB Adobe PDF Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3727615
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact