Estimation of weak ARMA models with regime changes

被引：2
|
作者：
Mainassara, Yacouba Boubacar [1 ]
Rabehasaina, Landy [1 ]
机构：
[1] Univ Bourgogne Franche Comte, CNRS, UMR 6623, Lab Math Besancon, 16 Route Gray, F-25030 Besancon, France
来源：
STATISTICAL INFERENCE FOR STOCHASTIC PROCESSES | 2020年 / 23卷 / 01期
关键词：
Least square estimation; Random coefficients; Weak ARMA models; LARGE-SAMPLE PROPERTIES; TIME-SERIES MODELS; VARMA MODELS; CONSISTENT; HETEROSKEDASTICITY; LIKELIHOOD; STATIONARITY;
D O I：
10.1007/s11203-019-09202-3
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 derive the asymptotic properties of the least squares estimator (LSE) of autoregressive moving-average (ARMA) models with regime changes under the assumption that the errors are uncorrelated but not necessarily independent. Relaxing the independence assumption considerably extends the range of application of the class of ARMA models with regime changes. Conditions are given for the consistency and asymptotic normality of the LSE. A particular attention is given to the estimation of the asymptotic covariance matrix, which may be very different from that obtained in the standard framework. The theoretical results are illustrated by means of Monte Carlo experiments.
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页码：1 / 52
页数：52
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