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dc.contributor.authorSu, Jen-Jeen_US
dc.contributor.authorCheung, Adrian Wai-Kongen_US
dc.contributor.authorRoca, Eduardoen_US
dc.date.accessioned2017-05-03T11:42:15Z
dc.date.available2017-05-03T11:42:15Z
dc.date.issued2013en_US
dc.date.modified2013-08-22T23:46:11Z
dc.identifier.issn15493644en_US
dc.identifier.doi10.3844/jmssp.2013.102.111en_US
dc.identifier.urihttp://hdl.handle.net/10072/52729
dc.description.abstractWe provide simulation evidence that shed light on several size and power issues in relation to lag selection of the augmented (nonlinear) KSS test. Two lag selection approaches are considered - the modified AIC (MAIC) approach of Ng and Perron (1995) and a sequential General to Specific (GS) testing approach proposed by Ng and Perron (2001). Either one of these approaches can be used to select the optimal lag based on either the augmented linear Dickey Fuller test or the augmented nonlinear KSS test, resulting in four possible selection methods, namely, MAIC, GS, NMAIC and NGS. The evidence suggests that the asymptotic critical values of the KSS test tends to result in over-sizing if the (N)GS method is used and under-sizing if the (N)MAIC method is utilised. Thus, we recommend that the critical values should be generated from finite samples. We also find evidence that the (N)MAIC method has less size distortion than the (N)GS method, suggesting that the MAIC-based KSS test is preferred. Interestingly, the MAIC-based KSS test with lag selection based on the linear ADF regression is generally more powerful than the test with lag selection based on the nonlinear version.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent130144 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.publisherScience Publicationsen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom102en_US
dc.relation.ispartofpageto111en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalJournal of Mathematics and Statisticsen_US
dc.relation.ispartofvolume9en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchApplied Statisticsen_US
dc.subject.fieldofresearchcode010401en_US
dc.titleLag Selection of the Augmented Kapetanios-Shin-Snell Nonlinear Unit Root Testen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Business School, Department of Accounting, Finance and Economicsen_US
gro.rights.copyright© The Author(s) 2013. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this journal please refer to the journal’s website or contact the authors.en_US
gro.date.issued2013
gro.hasfulltextFull Text


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