Block Bootstrap and Long Memory

George Kapetanios and Fotis Papailias
Additional contact information
Fotis Papailias: Queen Mary, University of London

No 679, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: We consider the issue of Block Bootstrap methods in processes that exhibit strong dependence. The main difficulty is to transform the series in such way that implementation of these techniques can provide an accurate approximation to the true distribution of the test statistic under consideration. The bootstrap algorithm we suggest consists of the following operations: given xt ~ I(d0), 1) estimate the long memory parameter and obtain dˆ, 2) difference the series dˆ times, 3) apply the block bootstrap on the above and finally, 4) cumulate the bootstrap sample dˆ times. Repetition of steps 3 and 4 for a sufficient number of times, results to a successful estimation of the distribution of the test statistic. Furthermore, we establish the asymptotic validity of this method. Its finite-sample properties are investigated via Monte Carlo experiments and the results indicate that it can be used as an alternative, and in most of the cases to be preferred than the Sieve AR bootstrap for fractional processes.

Keywords: Block Bootstrap, Long memory; Resampling, Strong dependence (search for similar items in EconPapers)
JEL-codes: C15 C22 C63 (search for similar items in EconPapers)
Date: 2011-06-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.qmul.ac.uk/sef/media/econ/research/wor ... 2011/items/wp679.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:679

Access Statistics for this paper

More papers in Working Papers from Queen Mary University of London, School of Economics and Finance Contact information at EDIRC.
Bibliographic data for series maintained by Nicholas Owen ( this e-mail address is bad, please contact ).