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October 2010 An efficient estimator for locally stationary Gaussian long-memory processes
Wilfredo Palma, Ricardo Olea
Ann. Statist. 38(5): 2958-2997 (October 2010). DOI: 10.1214/10-AOS812

Abstract

This paper addresses the estimation of locally stationary long-range dependent processes, a methodology that allows the statistical analysis of time series data exhibiting both nonstationarity and strong dependency. A time-varying parametric formulation of these models is introduced and a Whittle likelihood technique is proposed for estimating the parameters involved. Large sample properties of these Whittle estimates such as consistency, normality and efficiency are established in this work. Furthermore, the finite sample behavior of the estimators is investigated through Monte Carlo experiments. As a result from these simulations, we show that the estimates behave well even for relatively small sample sizes.

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Wilfredo Palma. Ricardo Olea. "An efficient estimator for locally stationary Gaussian long-memory processes." Ann. Statist. 38 (5) 2958 - 2997, October 2010. https://doi.org/10.1214/10-AOS812

Information

Published: October 2010
First available in Project Euclid: 20 August 2010

zbMATH: 1200.62109
MathSciNet: MR2722461
Digital Object Identifier: 10.1214/10-AOS812

Subjects:
Primary: 62M10
Secondary: 60G15

Keywords: asymptotic normality , consistency , efficiency , local stationarity , long-range dependence , nonstationarity , Whittle estimation

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.38 • No. 5 • October 2010
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