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

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.

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Abstract

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.

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ID Code: 32200
Item Type: Journal Article
Additional URLs:
Keywords: Bootstrap for time series, Long memory, GPH and LW estimator, Confidence intervals
DOI: 10.1080/00949650902849286
ISSN: 1563-5163
Subjects: Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > OTHER ECONOMICS (149900) > Economics not elsewhere classified (149999)
Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300) > Time-Series Analysis (140305)
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright 2009 Taylor & Francis
Deposited On: 24 May 2010 08:29
Last Modified: 29 Feb 2012 23:56

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