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- Publisher Website: 10.1016/j.jeconom.2019.08.009
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Article: Non-standard inference for augmented double autoregressive models with null volatility coefficients
Title | Non-standard inference for augmented double autoregressive models with null volatility coefficients |
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Authors | |
Keywords | Augmented DAR model DAR model Heavy-tailedness Non-standard asymptotics Parameter on the boundary |
Issue Date | 2020 |
Publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/jeconom |
Citation | Journal of Econometrics, 2020, v. 215 n. 1, p. 165-183 How to Cite? |
Abstract | This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows the applicable scope in studying heavy-tailed data. To overcome this deficiency, this paper develops a systematic statistical inference procedure based on the self-weighted GQMLE for the augmented DAR model. Except for the Lagrange multiplier test statistic, the Wald, quasi-likelihood ratio and portmanteau test statistics are all shown to have non-standard asymptotics. The entire procedure is valid as long as the data are stationary, and its usefulness is illustrated by simulation studies and one real example. |
Persistent Identifier | http://hdl.handle.net/10722/288460 |
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 | Jiang, F | - |
dc.contributor.author | Li, D | - |
dc.contributor.author | Zhu, K | - |
dc.date.accessioned | 2020-10-05T12:13:14Z | - |
dc.date.available | 2020-10-05T12:13:14Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Journal of Econometrics, 2020, v. 215 n. 1, p. 165-183 | - |
dc.identifier.issn | 0304-4076 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288460 | - |
dc.description.abstract | This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows the applicable scope in studying heavy-tailed data. To overcome this deficiency, this paper develops a systematic statistical inference procedure based on the self-weighted GQMLE for the augmented DAR model. Except for the Lagrange multiplier test statistic, the Wald, quasi-likelihood ratio and portmanteau test statistics are all shown to have non-standard asymptotics. The entire procedure is valid as long as the data are stationary, and its usefulness is illustrated by simulation studies and one real example. | - |
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.subject | Augmented DAR model | - |
dc.subject | DAR model | - |
dc.subject | Heavy-tailedness | - |
dc.subject | Non-standard asymptotics | - |
dc.subject | Parameter on the boundary | - |
dc.title | Non-standard inference for augmented double autoregressive models with null volatility coefficients | - |
dc.type | Article | - |
dc.identifier.email | Zhu, K: mazhuke@hku.hk | - |
dc.identifier.authority | Zhu, K=rp02199 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.jeconom.2019.08.009 | - |
dc.identifier.scopus | eid_2-s2.0-85072624175 | - |
dc.identifier.hkuros | 314990 | - |
dc.identifier.volume | 215 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 165 | - |
dc.identifier.epage | 183 | - |
dc.identifier.isi | WOS:000515194100007 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 0304-4076 | - |