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Article: Linear double autoregression

TitleLinear double autoregression
Authors
KeywordsConditional quantile estimation
Goodness-of-fit test
Heavy tail
Nonlinear time series model
Stationary solution
Issue Date2018
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom
Citation
Journal of Econometrics, 2018, v. 207 n. 1, p. 162-174 How to Cite?
AbstractThis paper proposes the linear double autoregression, a conditional heteroscedastic model with a conditional mean structure but compatible with the quantile regression. The existence of a strictly stationary solution is discussed, for which a necessary and sufficient condition is established. A doubly weighted quantile regression estimation procedure is introduced, where the first set of weights ensures the asymptotic normality of the estimator and the second set improves its efficiency through balancing individual quantile regression estimators across multiple quantile levels. Bayesian information criteria are proposed for model selection, and two goodness-of-fit tests are constructed to check the adequacy of the fitted conditional mean and conditional scale structures. Simulation studies indicate that the proposed inference tools perform well in finite samples, and an empirical example illustrates the usefulness of the new model.
Persistent Identifierhttp://hdl.handle.net/10722/263268
ISSN
2019 Impact Factor: 1.577
2015 SCImago Journal Rankings: 3.781
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhu, Q-
dc.contributor.authorZheng, Y-
dc.contributor.authorLi, G-
dc.date.accessioned2018-10-22T07:36:11Z-
dc.date.available2018-10-22T07:36:11Z-
dc.date.issued2018-
dc.identifier.citationJournal of Econometrics, 2018, v. 207 n. 1, p. 162-174-
dc.identifier.issn0304-4076-
dc.identifier.urihttp://hdl.handle.net/10722/263268-
dc.description.abstractThis paper proposes the linear double autoregression, a conditional heteroscedastic model with a conditional mean structure but compatible with the quantile regression. The existence of a strictly stationary solution is discussed, for which a necessary and sufficient condition is established. A doubly weighted quantile regression estimation procedure is introduced, where the first set of weights ensures the asymptotic normality of the estimator and the second set improves its efficiency through balancing individual quantile regression estimators across multiple quantile levels. Bayesian information criteria are proposed for model selection, and two goodness-of-fit tests are constructed to check the adequacy of the fitted conditional mean and conditional scale structures. Simulation studies indicate that the proposed inference tools perform well in finite samples, and an empirical example illustrates the usefulness of the new model.-
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.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectConditional quantile estimation-
dc.subjectGoodness-of-fit test-
dc.subjectHeavy tail-
dc.subjectNonlinear time series model-
dc.subjectStationary solution-
dc.titleLinear double autoregression-
dc.typeArticle-
dc.identifier.emailZheng, Y: yaozheng@connect.hku.hk-
dc.identifier.emailLi, G: gdli@hku.hk-
dc.identifier.authorityLi, G=rp00738-
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.jeconom.2018.05.006-
dc.identifier.scopuseid_2-s2.0-85050876340-
dc.identifier.hkuros293510-
dc.identifier.volume207-
dc.identifier.issue1-
dc.identifier.spage162-
dc.identifier.epage174-
dc.identifier.isiWOS:000447479900008-
dc.publisher.placeNetherlands-
dc.identifier.issnl0304-4076-

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