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2018 Least tail-trimmed absolute deviation estimation for autoregressions with infinite/finite variance
Rongning Wu, Yunwei Cui
Electron. J. Statist. 12(1): 941-959 (2018). DOI: 10.1214/18-EJS1390

Abstract

We propose least tail-trimmed absolute deviation estimation for autoregressive processes with infinite/finite variance. We explore the large sample properties of the resulting estimator and establish its asymptotic normality. Moreover, we study convergence rates of the estimator under different moment settings and show that it attains a super-$\sqrt{n}$ convergence rate when the innovation variance is infinite. Simulation studies are carried out to examine the finite-sample performance of the proposed method and that of relevant statistical inferences. A real example is also presented.

Citation

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Rongning Wu. Yunwei Cui. "Least tail-trimmed absolute deviation estimation for autoregressions with infinite/finite variance." Electron. J. Statist. 12 (1) 941 - 959, 2018. https://doi.org/10.1214/18-EJS1390

Information

Received: 1 December 2016; Published: 2018
First available in Project Euclid: 7 March 2018

zbMATH: 06864481
MathSciNet: MR3771371
Digital Object Identifier: 10.1214/18-EJS1390

Subjects:
Primary: 62M10
Secondary: 62F12

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Vol.12 • No. 1 • 2018
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