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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Bibliographic Details
Main Authors: Carroll, Raymond J. (Author) , Härdle, Wolfgang (Author) , Mammen, Enno (Author)
Format: Article (Journal)
Language:English
Published: 17 May 2002
In: Econometric theory
Year: 2002, Volume: 18, Issue: 4, Pages: 886-912
ISSN:1469-4360
DOI:10.1017/S0266466602184040
Online Access: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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Author Notes:Raymond J. Carroll, Wolfgang Härdle, Enno Mammen
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Summary: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.
Item Description:Gesehen am 06.02.2018
Physical Description:Online Resource
ISSN:1469-4360
DOI:10.1017/S0266466602184040