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Article: SIGNIFICANT VARIABLE SELECTION AND AUTOREGRESSIVE ORDER DETERMINATION FOR TIME-SERIES PARTIALLY LINEAR MODELS

TitleSIGNIFICANT VARIABLE SELECTION AND AUTOREGRESSIVE ORDER DETERMINATION FOR TIME-SERIES PARTIALLY LINEAR MODELS
Authors
Issue Date2014
PublisherWiley. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892
Citation
Journal of Time Series Analysis, 2014, v. 35, p. 478-490 How to Cite?
AbstractThis paper is concerned with the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation (SCAD) penalty for a partially linear model with time-series errors. By combining the profile semi-parametric least squares method and SCAD penalty technique, a new penalized estimation for the regression and autoregressive parameters in the model is proposed. We show that the asymptotic property of the resultant estimator is the same as if the order of autoregressive error structure and non-zero regression coefficients are known in advance, thus achieving the oracle property in the sense of Fan and Li (2001). In addition, based on a prewhitening technique, we construct a two-stage local linear estimator (TSLLE) for the non-parametric component. It is shown that the TSLLE is more asymtotically efficient than the one that ignores the autoregressive time-series error structure. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. An example of application on electricity usage data is also illustrated.
Persistent Identifierhttp://hdl.handle.net/10722/209823

 

DC FieldValueLanguage
dc.contributor.authorLi, Den_US
dc.contributor.authorLi, Gen_US
dc.contributor.authorYou, Jen_US
dc.date.accessioned2015-05-18T03:26:30Z-
dc.date.available2015-05-18T03:26:30Z-
dc.date.issued2014en_US
dc.identifier.citationJournal of Time Series Analysis, 2014, v. 35, p. 478-490en_US
dc.identifier.urihttp://hdl.handle.net/10722/209823-
dc.description.abstractThis paper is concerned with the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation (SCAD) penalty for a partially linear model with time-series errors. By combining the profile semi-parametric least squares method and SCAD penalty technique, a new penalized estimation for the regression and autoregressive parameters in the model is proposed. We show that the asymptotic property of the resultant estimator is the same as if the order of autoregressive error structure and non-zero regression coefficients are known in advance, thus achieving the oracle property in the sense of Fan and Li (2001). In addition, based on a prewhitening technique, we construct a two-stage local linear estimator (TSLLE) for the non-parametric component. It is shown that the TSLLE is more asymtotically efficient than the one that ignores the autoregressive time-series error structure. Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure. An example of application on electricity usage data is also illustrated.en_US
dc.languageengen_US
dc.publisherWiley. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892-
dc.relation.ispartofJournal of Time Series Analysisen_US
dc.rightsThis is the accepted version of the following article: Journal of Time Series Analysis, 2014, v. 35, p. 478-490 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12077/abstract-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleSIGNIFICANT VARIABLE SELECTION AND AUTOREGRESSIVE ORDER DETERMINATION FOR TIME-SERIES PARTIALLY LINEAR MODELSen_US
dc.typeArticleen_US
dc.identifier.emailLi, G: gdli@hku.hken_US
dc.identifier.authorityLi, G=rp00738en_US
dc.description.naturepostprint-
dc.identifier.doi10.1111/jtsa.12077en_US
dc.identifier.hkuros243285en_US
dc.identifier.volume35en_US
dc.identifier.spage478en_US
dc.identifier.epage490en_US
dc.publisher.placeUnited Kingdom-

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