Testing for Neglected Nonlinearity in Long Memory Models

Richard T. Baillie and George Kapetanios
Additional contact information
Richard T. Baillie: Queen Mary, University of London

No 528, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: This paper constructs tests for the presence of nonlinearity of unknown form in addition to a fractionally integrated, long memory component in a time series process. The tests are based on artificial neural network structures and do not restrict the parametric form of the nonlinearity. The tests only require a consistent estimate of the long memory parameter. Some theoretical results for the new tests are obtained and detailed simulation evidence is also presented on the power of the tests. The new methodology is then applied to a wide variety of economic and financial time series.

Keywords: Long memory; Non-linearity; Artificial neural networks; Realized volatility; Absolute returns; Real exchange rates; Unemployment (search for similar items in EconPapers)
JEL-codes: C12 C22 F31 (search for similar items in EconPapers)
Date: 2005-04-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.qmul.ac.uk/sef/media/econ/research/wor ... 2005/items/wp528.pdf (application/pdf)

Related works:
Journal Article: Testing for Neglected Nonlinearity in Long-Memory Models (2007)
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:528

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 (n.j.owen@qmul.ac.uk this e-mail address is bad, please contact repec@repec.org).