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 language | English |
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Article number | 20190009 |
Pages (from-to) | 1-39 |
Number of pages | 39 |
Journal | Journal of time series econometrics |
Volume | 12 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2020 |
Keywords
- curve process
- dynamic functional principal component analysis
- functional ARFIMA
- long-run covariance
- long-range dependence