A Test for Serial Dependence Using Neural Networks

George Kapetanios

No 609, Working Papers from Queen Mary University of London, School of Economics and Finance

Abstract: Testing serial dependence is central to much of time series econometrics. A number of tests that have been developed and used to explore the dependence properties of various processes. This paper builds on recent work on nonparametric tests of independence. We consider a fact that characterises serially dependent processes using a generalisation of the autocorrelation function. Using this fact we build dependence tests that make use of neural network based approximations. We derive the theoretical properties of our tests and show that they have superior power properties. Our Monte Carlo evaluation supports the theoretical findings. An application to a large dataset of stock returns illustrates the usefulness of the proposed tests.

Keywords: Independence; Neural networks; Strict stationarity; Bootstrap; S&P500 (search for similar items in EconPapers)
JEL-codes: C32 C33 G12 (search for similar items in EconPapers)
Date: 2007-10-01
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.qmul.ac.uk/sef/media/econ/research/wor ... 2007/items/wp609.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:609

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 ).