The Annals of Statistics
- Ann. Statist.
- Volume 38, Number 5 (2010), 2958-2997.
An efficient estimator for locally stationary Gaussian long-memory processes
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.
Ann. Statist., Volume 38, Number 5 (2010), 2958-2997.
First available in Project Euclid: 20 August 2010
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]
Secondary: 60G15: Gaussian processes
Palma, Wilfredo; Olea, Ricardo. An efficient estimator for locally stationary Gaussian long-memory processes. Ann. Statist. 38 (2010), no. 5, 2958--2997. doi:10.1214/10-AOS812. https://projecteuclid.org/euclid.aos/1282315405
- Supplementary material: Supplement to "An Efficient Estimator for Locally Stationary Gaussian Long-Memory Processes".