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Article: Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model

TitleAdaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model
Authors
KeywordsAdaptive inference
Lagrange multiplier test
Portmanteau test
QMLE
Semiparametric BEKK model
Semiparametric GARCH model
Issue Date2021
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom
Citation
Journal of Econometrics, 2021, v. 224 n. 2, p. 306-329 How to Cite?
AbstractThis paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type short run component by the quasi maximum likelihood estimator (QMLE). We show that the QMLE is asymptotically normal with the parametric convergence rate. Next, we construct a Lagrange multiplier test for linear parameter constraint and a portmanteau test for model checking, and obtain their asymptotic null distributions. Our entire statistical inference procedure works for the non-stationary data with two important features: first, our QMLE and two tests are adaptive to the unknown form of the long run component; second, our QMLE and two tests share the same efficiency and testing power as those in variance targeting method when the S-GARCH model is stationary.
Persistent Identifierhttp://hdl.handle.net/10722/305035
ISSN
2023 Impact Factor: 9.9
2023 SCImago Journal Rankings: 9.161
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, F-
dc.contributor.authorLi, D-
dc.contributor.authorZhu, K-
dc.date.accessioned2021-10-05T02:38:49Z-
dc.date.available2021-10-05T02:38:49Z-
dc.date.issued2021-
dc.identifier.citationJournal of Econometrics, 2021, v. 224 n. 2, p. 306-329-
dc.identifier.issn0304-4076-
dc.identifier.urihttp://hdl.handle.net/10722/305035-
dc.description.abstractThis paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type short run component by the quasi maximum likelihood estimator (QMLE). We show that the QMLE is asymptotically normal with the parametric convergence rate. Next, we construct a Lagrange multiplier test for linear parameter constraint and a portmanteau test for model checking, and obtain their asymptotic null distributions. Our entire statistical inference procedure works for the non-stationary data with two important features: first, our QMLE and two tests are adaptive to the unknown form of the long run component; second, our QMLE and two tests share the same efficiency and testing power as those in variance targeting method when the S-GARCH model is stationary.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom-
dc.relation.ispartofJournal of Econometrics-
dc.subjectAdaptive inference-
dc.subjectLagrange multiplier test-
dc.subjectPortmanteau test-
dc.subjectQMLE-
dc.subjectSemiparametric BEKK model-
dc.subjectSemiparametric GARCH model-
dc.titleAdaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model-
dc.typeArticle-
dc.identifier.emailZhu, K: mazhuke@hku.hk-
dc.identifier.authorityZhu, K=rp02199-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jeconom.2020.10.007-
dc.identifier.scopuseid_2-s2.0-85097449868-
dc.identifier.hkuros326289-
dc.identifier.volume224-
dc.identifier.issue2-
dc.identifier.spage306-
dc.identifier.epage329-
dc.identifier.isiWOS:000689638900004-
dc.publisher.placeNetherlands-

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