Rank-based Entropy Tests for Serial Independence

Cees Diks () and Valentyn Panchenko

Studies in Nonlinear Dynamics & Econometrics, 2008, vol. 12, issue 1, 21

Abstract: In nonparametric tests for serial independence the marginal distribution of the data acts as an infinite dimensional nuisance parameter. The decomposition of joint distributions in terms of a copula density and marginal densities shows that in general empirical marginals carry no information on dependence. It follows that the order of ranks is sufficient for inference, which motivates transforming the data to a pre-specified marginal distribution prior to testing. As a test statistic we use an estimator of the marginal redundancy. We numerically study the finite sample properties of the tests obtained when the data are transformed to uniform as well as normal marginals. For comparison purposes we also derive a rank-based test against local ARCH alternatives. The performance of the new tests is compared with a modified version of the BDS test and with the Ljung-Box test.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.2202/1558-3708.1476 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Rank-based entropy tests for serial independence (2006)
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:sndecm:v:12:y:2008:i:1:n:2

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

DOI: 10.2202/1558-3708.1476

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().