Bootstrap approaches for estimation and confidence intervals of long term memory processes


Autoria(s): Bisaglia, Luisa; Bordignon, Silvano; Cecchinato, Nedda
Data(s)

2009

Resumo

In this work, we investigate an alternative bootstrap approach based on a result of Ramsey [F.L. Ramsey, Characterization of the partial autocorrelation function, Ann. Statist. 2 (1974), pp. 1296-1301] and on the Durbin-Levinson algorithm to obtain a surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semi-parametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from normality. The approach is also useful to estimate confidence intervals for the memory parameter d by improving the coverage level of the interval.

Identificador

http://eprints.qut.edu.au/32200/

Publicador

Taylor and Francis

Relação

DOI:10.1080/00949650902849286

Bisaglia, Luisa, Bordignon, Silvano, & Cecchinato, Nedda (2009) Bootstrap approaches for estimation and confidence intervals of long term memory processes. Journal of Statistical Computation and Simulation.

Direitos

Copyright 2009 Taylor & Francis

Fonte

QUT Business School; School of Economics & Finance

Palavras-Chave #149999 Economics not elsewhere classified #140305 Time-Series Analysis #Bootstrap for time series #Long memory #GPH and LW estimator #Confidence intervals
Tipo

Journal Article