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dc.contributor.authorBlake, Andrew P.en_US
dc.contributor.authorKapetanios, Georgeen_US
dc.date.accessioned2012-09-20T12:59:13Z-
dc.date.available2012-09-20T12:59:13Z-
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/10419/62815-
dc.description.abstractTests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejections of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little work on the extent of the effect of neglected nonlinearity on the properties of ARCH tests. This paper provides some such evidence and also new ARCH testing procedures that are robust to the presence of neglected nonlinearity. Monte Carlo evidence shows that the problem is serious and that the new methods alleviate this problem to a very large extent.en_US
dc.language.isoengen_US
dc.publisherQueen Mary, Univ. of London, Dep. of Economics Londonen_US
dc.relation.ispartofseriesWorking Paper, Department of Economics, Queen Mary, University of London 496en_US
dc.subject.jelC12en_US
dc.subject.jelC22en_US
dc.subject.jelC45en_US
dc.subject.ddc330en_US
dc.subject.keywordNonlinearity, ARCH, Neural networksen_US
dc.subject.stwARCH-Modellen_US
dc.subject.stwNeuronale Netzeen_US
dc.subject.stwNichtlineares Verfahrenen_US
dc.titleTesting for ARCH in the presence of nonlinearity of unknow form in the conditional meanen_US
dc.typeWorking Paperen_US
dc.identifier.ppn377046299en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
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