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

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
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3727615
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