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Article: Linear double autoregression
Title | Linear double autoregression |
---|---|
Authors | |
Keywords | Conditional quantile estimation Goodness-of-fit test Heavy tail Nonlinear time series model Stationary solution |
Issue Date | 2018 |
Publisher | Elsevier 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? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/263268 |
ISSN | 2023 Impact Factor: 9.9 2023 SCImago Journal Rankings: 9.161 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhu, Q | - |
dc.contributor.author | Zheng, Y | - |
dc.contributor.author | Li, G | - |
dc.date.accessioned | 2018-10-22T07:36:11Z | - |
dc.date.available | 2018-10-22T07:36:11Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Journal of Econometrics, 2018, v. 207 n. 1, p. 162-174 | - |
dc.identifier.issn | 0304-4076 | - |
dc.identifier.uri | http://hdl.handle.net/10722/263268 | - |
dc.description.abstract | This 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.language | eng | - |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom | - |
dc.relation.ispartof | Journal of Econometrics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Conditional quantile estimation | - |
dc.subject | Goodness-of-fit test | - |
dc.subject | Heavy tail | - |
dc.subject | Nonlinear time series model | - |
dc.subject | Stationary solution | - |
dc.title | Linear double autoregression | - |
dc.type | Article | - |
dc.identifier.email | Zheng, Y: yaozheng@connect.hku.hk | - |
dc.identifier.email | Li, G: gdli@hku.hk | - |
dc.identifier.authority | Li, G=rp00738 | - |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1016/j.jeconom.2018.05.006 | - |
dc.identifier.scopus | eid_2-s2.0-85050876340 | - |
dc.identifier.hkuros | 293510 | - |
dc.identifier.volume | 207 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 162 | - |
dc.identifier.epage | 174 | - |
dc.identifier.isi | WOS:000447479900008 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 0304-4076 | - |