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Identifier to cite or link to this item: https://hdl.handle.net/10086/16959

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Title
Nonparametric Density Estimation for Linear Processes with Infinite Variance
Creator
Creator Name
Honda, Toshio Honda, Toshio
本田, 敏雄 本田, 敏雄
Affiliation
Affiliation Name
Hitotsubashi University
Key Word
linear processes
kernel density estimator
domain of attraction
stable distribution
noncentral limit theorem
martingale central limit theorem
Abstract (distribution restricted)
We consider nonparametric estimation of marginal density functions of linear processes by using kernel density estimators. We assume that the innovation processes are i.i.d. and have infinite-variance. We present the asymptotic distributions of the kernel density estimators with the order of bandwidths fixed as h=cn-1/5, where n is the sample size. The asymptotic distributions depend on both the coefficients of linear processes and the tail behavior of the innovations. In some cases, the kernel estimators have the same asymptotic distributions as for i.i.d. observations. In other cases, the normalized kernel density estimators converge in distribution to stable distributions. A simulation study is also carried out to examine small sample properties.
Description
February 2006; August 2006 (Revised)
Publisher
Graduate School of Economics, Hitotsubashi University
Issued Date
2006-08
Language
English(eng)
Resource Type
technical report
Version Type
VoR
Is Part OfRelation -
Related Title
Discussion papers ; No. 2005-13
Number of Pages
34
Edition
[Revised version]
Appears in Collections
Discussion papers / Graduate School of Economics, Hitotsubashi University
URL
https://hdl.handle.net/10086/16959