A Comparison of Hurst Exponent Estimators in Long-range Dependent Curve Time Series

Han Lin Shang

Journal of Time Series Econometrics, 2020, vol. 12, issue 1, 39

Abstract: The Hurst exponent is the simplest numerical summary of self-similar long-range 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 time- and frequency-domain Hurst exponent estimators and make our recommendations.

Keywords: curve process; dynamic functional principal component analysis; functional ARFIMA; long-run covariance; long-range dependence (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/jtse-2019-0009

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