Efficiency comparisons of maximum-likelihood-based estimators in GARCH models.
In this paper, we investigate the loss of asymptotic efficiency of semiparametric and quasi-maximum-likelihood estimators relative to maximum-likelihood estimators in models with generalized autoregressive conditional heteroscedasticity (GARCH). For a general time-varying location-scale model, the...
| Publicado en: | Journal of Econometrics Vol. 93; no. 1; pp. 93 - 112 |
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| Autores principales: | , |
| Formato: | Artículo |
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Elsevier Science
November 1999
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=512898026&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 512898026 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03044076 ECM jtl: Journal of Econometrics issn: 03044076 maglogo: N pubinfo: dt: November 1999 vid: 93 iid: 1 pid: 1004 pub: Elsevier Science artinfo: ui: 512898026 10.1016/S0304-4076(99)00005-6 ppf: 93 ppct: 19 formats: tig: atl: Efficiency comparisons of maximum-likelihood-based estimators in GARCH models. aug: au: González-Rivera, Gloria Drost, Feike C. su: Maximum likelihood statistics ARCH model (Econometrics) Parameter estimation sug: subj: Maximum likelihood statistics ARCH model (Econometrics) Parameter estimation ab: In this paper, we investigate the loss of asymptotic efficiency of semiparametric and quasi-maximum-likelihood estimators relative to maximum-likelihood estimators in models with generalized autoregressive conditional heteroscedasticity (GARCH). For a general time-varying location-scale model, the factors that contribute to differences in efficiency among the estimators can be divided in two categories. One pertains to the parametric specifications of the conditional mean and the conditional variance. The other corresponds to the shape characteristics of the conditional density of the standardized errors, summarized in the coefficients of skewness and kurtosis together with the Fisher information for location and scale. The quantification of these factors has practical implications since it can help to decide if the more complex semiparametric estimator provides sufficient efficiency gains with respect to the simplest quasi-maximum-likelihood estimator. We also prove that there is no probability density function, with the exception of the normal, for which the asymptotic efficiency of the three estimators is the same. Particular models are also considered, for which the efficiency comparisons are greatly simplified. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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