A Test of the Long Memory Hypothesis Based on Self-Similarity

Davidson James () and Dooruj Rambaccussing
Additional contact information
Davidson James: Economics, University of Exeter, Exeter, Devon, UK

Journal of Time Series Econometrics, 2015, vol. 7, issue 2, 115-141

Abstract: This paper develops a new test of true versus spurious long memory, based on log-periodogram estimation of the long memory parameter using skip-sampled data. A correction factor is derived to overcome the bias in this estimator due to aliasing. The procedure is designed to be used in the context of a conventional test of significance of the long memory parameter, and a composite test procedure is described that has the properties of known asymptotic size and consistency. The test is implemented using the bootstrap, with the distribution under the null hypothesis being approximated using a dependent-sample bootstrap technique to approximate short-run dependence following fractional differencing. The properties of the test are investigated in a set of Monte Carlo experiments. The procedure is illustrated by applications to exchange rate volatility and dividend growth series.

Keywords: long memory; skip-sampling; log-periodogram regression (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://doi.org/10.1515/jtse-2013-0036 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: A test of the long memory hypothesis based on self-similarity (2015)
Working Paper: A test of long memory hypothesis based on self-similarity (2015)
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:bpj:jtsmet:v:7:y:2015:i:2:p:115-141:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jtse/html

DOI: 10.1515/jtse-2013-0036

Access Statistics for this article

Journal of Time Series Econometrics is currently edited by Javier Hidalgo

More articles in Journal of Time Series Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().