Estimation in an additive model when the components are linked parametrically

Motivated by a nonparametric GARCH model we consider nonparametric additive autoregression models in the special case that the additive components are linked parametrically. We show that the parameter can be estimated with parametric rate and give the normal limit. Our procedure is based on two step...

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Hauptverfasser: Carroll, Raymond J. (VerfasserIn) , Härdle, Wolfgang (VerfasserIn) , Mammen, Enno (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 17 May 2002
In: Econometric theory
Year: 2002, Jahrgang: 18, Heft: 4, Pages: 886-912
ISSN:1469-4360
DOI:10.1017/S0266466602184040
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1017/S0266466602184040
Verlag, Volltext: https://www.cambridge.org/core/journals/econometric-theory/article/estimation-in-an-additive-model-when-the-components-are-linked-parametrically/4F3A9BA80553D3052855C0DF9A6514B7
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Verfasserangaben:Raymond J. Carroll, Wolfgang Härdle, Enno Mammen
Beschreibung
Zusammenfassung:Motivated by a nonparametric GARCH model we consider nonparametric additive autoregression models in the special case that the additive components are linked parametrically. We show that the parameter can be estimated with parametric rate and give the normal limit. Our procedure is based on two steps. In the first step nonparametric smoothers are used for the estimation of each additive component without taking into account the parametric link of the functions. In a second step the parameter is estimated by using the parametric restriction between the additive components. Interestingly, our method needs no undersmoothing in the first step.
Beschreibung:Gesehen am 06.02.2018
Beschreibung:Online Resource
ISSN:1469-4360
DOI:10.1017/S0266466602184040