Nonlinear least squares estimation of the periodic EXPAR(1) model

被引：0
|
作者：
Becila, S. [1 ]
Merzougui, M. [1 ]
机构：
[1] Univ Badji Mokhtar Annaba, LaPS Lab, Annaba, Algeria
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2022年 / 51卷 / 15期
关键词：
Nonlinear time series; periodic exponential autoregressive model; nonlinear least squares; asymptotic normality; consistency;
D O I：
10.1080/03610926.2020.1839099
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：
In this paper, we study the strong consistency and asymptotic normality properties of nonlinear least squares (NLS) estimator of the periodic EXPAR(1) model. The general statistical literature on estimation of nonlinear models of Gallant and White is used. Simulation study and one real example are given to assess the performance of this NLS.
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页码：5369 / 5381
页数：13
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