Ergodicity, geometric ergodicity and mixing conditions for nonparametric ARMA processes

被引：0

作者：

Graciela Boente

Ricardo Fraiman

机构：

[1] Universidad de Buenos Aires and conicet,

[2] ¶Buenos Aires¶ARGENTINA,undefined

[3] Universidad de la República,undefined

[4] ¶Montevideo¶URUGUAY,undefined

来源：

Bulletin of the Brazilian Mathematical Society | 2002年 / 33卷

关键词：

Keywords: ARMA models, geometric ergodicity, Fisher-consistency, kernel estimates.; Mathematical subject classification: 62G08, 37A25.;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this paper, we introduce nonparametric ARMA models which provide an alternative to nonparametric autoregressive models, when there is a large dependence to the past observations. Conditions for ergodicity and geometric ergodicity are given when both the nonparametric autoregressive part and themoving average structure depend only one step behind. Also, a Fisher-consistent procedure is provided and its performance is studied through a simulated example.

引用

页码：307 / 318

页数：11

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