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Article: Statistical inference for autoregressive models under heteroscedasticity of unknown form
Title | Statistical inference for autoregressive models under heteroscedasticity of unknown form |
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Authors | |
Keywords | Adaptive estimator autoregressive model conditional heteroscedasticity heteroscedasticity weighted least absolute deviations estimator |
Issue Date | 2019 |
Publisher | Institute of Mathematical Statistics. The Journal's web site is located at http://www.imstat.org/aos/ |
Citation | The Annals of Statistics, 2019, v. 47 n. 6, p. 3185-3215 How to Cite? |
Abstract | This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the asymptotic normality of the weighted least absolute deviations estimator (LADE) for the model. Second, we develop the random weighting (RW) method to estimate its asymptotic covariance matrix, leading to the implementation of the Wald test. Third, we construct a portmanteau test for model checking, and use the RW method to obtain its critical values. As a special weighted LADE, the feasible adaptive LADE (ALADE) is proposed and proved to have the same efficiency as its infeasible counterpart. The importance of our entire methodology based on the feasible ALADE is illustrated by simulation results and the real data analysis on three U.S. economic data sets. |
Persistent Identifier | http://hdl.handle.net/10722/280010 |
ISSN | 2021 Impact Factor: 4.904 2020 SCImago Journal Rankings: 5.877 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhu, K | - |
dc.date.accessioned | 2019-12-23T08:24:55Z | - |
dc.date.available | 2019-12-23T08:24:55Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | The Annals of Statistics, 2019, v. 47 n. 6, p. 3185-3215 | - |
dc.identifier.issn | 0090-5364 | - |
dc.identifier.uri | http://hdl.handle.net/10722/280010 | - |
dc.description.abstract | This paper provides an entire inference procedure for the autoregressive model under (conditional) heteroscedasticity of unknown form with a finite variance. We first establish the asymptotic normality of the weighted least absolute deviations estimator (LADE) for the model. Second, we develop the random weighting (RW) method to estimate its asymptotic covariance matrix, leading to the implementation of the Wald test. Third, we construct a portmanteau test for model checking, and use the RW method to obtain its critical values. As a special weighted LADE, the feasible adaptive LADE (ALADE) is proposed and proved to have the same efficiency as its infeasible counterpart. The importance of our entire methodology based on the feasible ALADE is illustrated by simulation results and the real data analysis on three U.S. economic data sets. | - |
dc.language | eng | - |
dc.publisher | Institute of Mathematical Statistics. The Journal's web site is located at http://www.imstat.org/aos/ | - |
dc.relation.ispartof | The Annals of Statistics | - |
dc.subject | Adaptive estimator | - |
dc.subject | autoregressive model | - |
dc.subject | conditional heteroscedasticity | - |
dc.subject | heteroscedasticity | - |
dc.subject | weighted least absolute deviations estimator | - |
dc.title | Statistical inference for autoregressive models under heteroscedasticity of unknown form | - |
dc.type | Article | - |
dc.identifier.email | Zhu, K: mazhuke@hku.hk | - |
dc.identifier.authority | Zhu, K=rp02199 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1214/18-AOS1775 | - |
dc.identifier.scopus | eid_2-s2.0-85066984638 | - |
dc.identifier.hkuros | 308833 | - |
dc.identifier.volume | 47 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 3185 | - |
dc.identifier.epage | 3215 | - |
dc.identifier.isi | WOS:000493896800007 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 0090-5364 | - |