Tests for Serial Independence and Linearity Based on Correlation Integrals

Cees Diks () and Manzan Sebastiano
Additional contact information
Manzan Sebastiano: CeNDEF - University of Amsterdam

Studies in Nonlinear Dynamics & Econometrics, 2002, vol. 6, issue 2, 22

Abstract: We propose information theoretic tests for serial independence and linearity in time series against nonlinear dependence on lagged variables, based on the conditional mutual information. The conditional mutual information, which is a general measure for dependence, is estimated using the correlation integral from chaos theory. The significance of the test statistics is determined by means of bootstrap methods. The size and power properties of the tests are examined by simulation and illustrated with applications to real US GNP data.

Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://doi.org/10.2202/1558-3708.1005 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Tests for serial independence and linearity based on correlation integrals (2001)
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:6:y:2002:i:2:n:2

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

DOI: 10.2202/1558-3708.1005

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 ().