BOOTSTRAPPING PERIODICALLY AUTOREGRESSIVE MODELS

被引：1

作者：

Ciolek, Gabriela ^{[1
,2
]}

Potorski, Pawel ^{[3
]}

机构：

[1] AGH Univ Sci & Technol, Al Mickiewicza 30, PL-30059 Krakow, Poland

[2] Univ Paris Saclay, Telecom ParisTech, LTCI, 46 Rue Barrault, F-75013 Paris, France

[3] AGH Univ Sci & Technol, Al Mickiewicza 30, PL-30059 Krakow, Poland

来源：

ESAIM-PROBABILITY AND STATISTICS | 2018年 / 21卷

关键词：

Bootstrap; least squares estimation; periodically autoregressive models; time series; BLOCK BOOTSTRAP; TIME-SERIES;

D O I：

10.1051/ps/2017017

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：

The main objective of this paper is to establish the residual and the wild bootstrap procedures for periodically autoregressive models. We use the least squares estimators of model's parameters and generate their bootstrap equivalents. We prove that the bootstrap procedures for causal periodic autoregressive time series with finite fourth moments are weakly consistent. Finally, we confirm our theoretical considerations by simulations.

引用

页码：394 / 411

页数：18

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