An efficient estimator for locally stationary Gaussian long-memory processes

Details An efficient estimator for locally stationary Gaussian long-memory processes An efficient estimator for locally stationary Gaussian long-memory processes

2010-11-11

arxiv.org/abs/1011.2607v1

Annals of Statistics 2010, Vol. 38, No. 5, 2958-2997

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/10-AOS812 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/10-AOS812

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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