Consistency of a nonparametric least squares estimator in integer-valued GARCH models

被引：2

作者：

Wechsung, Maximilian ^{[1
,2
]}

Neumann, Michael H. ^{[2
]}

机构：

[1] Charite Univ Med Berlin, Inst Biometrie & Klin Epidemiol, Charitepl 1, D-10117 Berlin, Germany

[2] Friedrich Schiller Univ Jena, Inst Math, Ernst Abbe Pl 2, D-07743 Jena, Germany

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2022年 / 34卷 / 02期

关键词：

INGARCH; nonparametric least squares; empirical process; mixing; ABSOLUTE REGULARITY;

D O I：

10.1080/10485252.2022.2043310

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We consider a nonparametric version of the integer-valued GARCH(1,1) model for time series of counts. The link function in the recursion for the variances is not specified by finite-dimensional parameters. Instead we impose nonparametric smoothness conditions. We propose a least squares estimator for this function and show that it is consistent with a rate that we conjecture to be nearly optimal.

引用

页码：491 / 519

页数：29

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