Asymptotic inference of least absolute deviation estimation for AR(1) processes

被引:3
|
作者
Wang, Xinghui [1 ,2 ]
Wang, Huilong [1 ]
Wang, Hongrui [1 ]
Hu, Shuhe [1 ]
机构
[1] Anhui Univ, Dept Stat, Hefei 230601, Anhui, Peoples R China
[2] Anhui Univ, Inst Innovat Dev Strategy, Hefei, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
Uniform limit; nearly stationary autoregression; mildly explosive autoregression; least absolute deviation estimation; MILDLY EXPLOSIVE AUTOREGRESSION; MODERATE DEVIATIONS; LIMIT THEORY; LAD ESTIMATION; REGRESSION; PARAMETER;
D O I
10.1080/03610926.2018.1549252
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 article, we consider a first-order autoregressive process y(t) = rho(n)y(t-1) + u(t) with n vertical bar 1-rho(n)vertical bar -> infinity as n -> infinity. The Gaussian limit theory and the Cauchy limit theory of the least absolute deviation estimator for the near-stationary process (rho(n) is an element of [0; 1)) and the mildly explosive process (rho(n)>1) are derived, respectively. The results are complementary to the uniform limit theory of least squares estimators for stationary autoregressions in Giraitis and Phillips (2006). Some simulations are carried out to assess the performance of our procedure.
引用
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页码:809 / 826
页数:18
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