title: Long-memory analysis of time series with missing values.
abstract: The estimation of long memory is often restricted by missing data. We examine the effects on the estimation of long memory of three simple gap-filling techniques: interpolation, random, and mean filling. Numerical simulations show that the gap-filling techniques introduce significant deviations from the expected scaling behavior for both persistent and antipersistent time series. For persistent time series the interpolation method provides a reliable estimation of long memory for scales longer than the largest likely gap.
authors.affiliation: Space and Atmospheric Physics, Blackett Laboratory, Imperial College, London, SW7 2BW, United Kingdom. paul.wilson@imperial.ac.uk
authors.forename: P S
authors.initials: PS
authors.lastname: Wilson
authors.forename: A C
authors.initials: AC
authors.lastname: Tomsett
authors.forename: R
authors.initials: R
authors.lastname: Toumi