A comparison of Hurst exponent estimators in long-range dependent curve time series

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Abstract

The Hurst exponent is the simplest numerical summary of self-similarlongrange dependent stochastic processes. We consider the estimation of Hurst exponent in long-range dependent curve time series. Our estimation method begins by constructing an estimate of the long-run covariance function, which we use, via dynamic functional principal component analysis, in estimating the orthonormal functions spanning the dominant sub-space of functional time series. Within the context of functional autoregressive fractionally integrated moving average (ARFIMA) models, we compare finite-sample bias, variance and mean square error among some timeand frequency-domain Hurst exponent estimators and make our recommendations.
Original languageEnglish
Article number20190009
Pages (from-to)1-39
Number of pages39
JournalJournal of time series econometrics
Volume12
Issue number1
DOIs
Publication statusPublished - Jan 2020

Keywords

  • curve process
  • dynamic functional principal component analysis
  • functional ARFIMA
  • long-run covariance
  • long-range dependence

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