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dc.contributor.authorSjölander, Pärde
dc.date.accessioned2011-04-26T02:58:00Zde
dc.date.accessioned2012-08-29T23:11:08Z
dc.date.available2012-08-29T23:11:08Z
dc.date.issued2010de
dc.identifier.urihttp://www.ssoar.info/ssoar/handle/document/24653
dc.description.abstractEngle's (1982) ARCH-LM test is the standard test to detect autoregressive conditional heteroscedasticity. In this paper, Monte Carlo simulations are used to demonstrate that the test's statistical size is biased in finite samples. Two complementing remedies to the related problems are proposed. One simple solution is to simulate new unbiased critical values for the ARCH-LM test. A second solution is based on the observation that for econometrics practitioners, detection of ARCH is generally followed by remedial modeling of this time-varying heteroscedasticity by the most general and robust model in the ARCH family; the GARCH(1,1) model. If the GARCH model's stationarity constraints are violated, as in fact is very often the case, obviously, we can conclude that ARCH-LM’s detection of conditional heteroscedasticity has no or limited practical value. Therefore, formulated as a function of whether the GARCH model's stationarity constraints are satisfied or not, an unbiased and more relevant two-step ARCH-LM test is specified. If the primary objectives of the study are to detect and remedy the problems of conditional heteroscedasticity, or to interpret GARCH parameters, the use of this paper’s new two-step procedure, 2S-UARCH-LM, is strongly recommended.en
dc.languageende
dc.subject.ddcWirtschaftde
dc.subject.ddcEconomicsen
dc.subject.otherARCH-LM; GARCH; Non-negativity Constraints; Stationarity Constraints
dc.titleA stationary unbiased finite sample ARCH-LM test procedureen
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalApplied Economicsde
dc.source.volume43de
dc.publisher.countryUSA
dc.source.issue8de
dc.subject.classozEconomic Statistics, Econometrics, Business Informaticsen
dc.subject.classozWirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatikde
dc.identifier.urnurn:nbn:de:0168-ssoar-246531de
dc.date.modified2011-04-26T10:24:00Zde
dc.rights.licencePEER Licence Agreement (applicable only to documents from PEER project)de
dc.rights.licencePEER Licence Agreement (applicable only to documents from PEER project)en
ssoar.gesis.collectionSOLIS;ADISde
ssoar.contributor.institutionhttp://www.peerproject.eu/de
internal.status3de
dc.type.stockarticlede
dc.type.documentjournal articleen
dc.type.documentZeitschriftenartikelde
dc.rights.copyrightfde
dc.source.pageinfo1019-1033
internal.identifier.classoz10905
internal.identifier.journal21de
internal.identifier.document32
internal.identifier.ddc330
dc.identifier.doihttps://doi.org/10.1080/00036840802600046de
dc.description.pubstatusPostprinten
dc.description.pubstatusPostprintde
internal.identifier.licence7
internal.identifier.pubstatus2
internal.identifier.review1
internal.check.abstractlanguageharmonizerCERTAIN
internal.check.languageharmonizerCERTAIN_RETAINED


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