Asymptotic inference of least absolute deviation estimation for AR(1) processes. Issue 4 (16th February 2020)

• Record Type: Journal Article
• Authors: Wang, Xinghui; Wang, Huilong; Wang, Hongrui; Hu, Shuhe
• Is Part Of: Communications in statistics. Volume 49:Issue 4(2020)
• Page Start: 809
• Page End: 826
• Publication Date: 2020-02-16
• Subjects: Uniform limit -- nearly stationary autoregression -- mildly explosive autoregression -- least absolute deviation estimation; 60G10 -- 62M10 -- 62F12; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2018.1549252
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: In this article, we consider a first-order autoregressive process y t = ρ n y t − 1 + u t with n | 1 − ρ n | → ∞ as n → ∞ . The Gaussian limit theory and the Cauchy limit theory of the least absolute deviation estimator for the near-stationary process ( ρ n ∈ [ 0, 1 ) ) and the mildly explosive process ( ρ 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.