Wavelet Estimation Performance of Fractional Integrated Processes with Heavy-Tails

Heni Boubaker ()
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Heni Boubaker: Technopolis Rabat-Shore

Computational Economics, 2020, vol. 55, issue 2, No 4, 473-498

Abstract: Abstract In this paper, we investigate the performance of four semi-parametric estimators in the wavelet domain in order to estimate the parameter of stationary long-memory models. The goal is to consider a wavelet estimate for the fractional differencing parameter d where the time series exhibit heavy tails. We show by Monte Carlo experiments that the wavelet Exact Local Whittle-type estimator improves considerably the other suggested wavelet-based estimators in terms of smaller bias, Root Mean Squared Error and variance. Furthermore, the simulation results show that the wavelet periodogram estimators perform better in most cases than wavelet ordinary least square estimate methods when the sample size is increased.

Keywords: Long-memory; Wavelet estimation; Heavy tails; Stable distributions; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C13 C15 C22 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-019-09897-9

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