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Econometrics 2018, 6(1), 13; https://doi.org/10.3390/econometrics6010013

An Overview of Modified Semiparametric Memory Estimation Methods

Institute of Statistics, Faculty of Economics and Management, Leibniz University Hannover, D-30167 Hannover, Germany
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Received: 13 December 2017 / Revised: 6 February 2018 / Accepted: 22 February 2018 / Published: 12 March 2018
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Abstract

Several modified estimation methods of the memory parameter have been introduced in the past years. They aim to decrease the upward bias of the memory parameter in cases of low frequency contaminations or an additive noise component, especially in situations with a short-memory process being contaminated. In this paper, we provide an overview and compare the performance of nine semiparametric estimation methods. Among them are two standard methods, four modified approaches to account for low frequency contaminations and three procedures developed for perturbed fractional processes. We conduct an extensive Monte Carlo study for a variety of parameter constellations and several DGPs. Furthermore, an empirical application of the log-absolute return series of the S&P 500 shows that the estimation results combined with a long-memory test indicate a spurious long-memory process. View Full-Text
Keywords: spurious long memory; semiparametric estimation; low frequency contamination; perturbation; Monte Carlo simulation spurious long memory; semiparametric estimation; low frequency contamination; perturbation; Monte Carlo simulation
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Busch, M.; Sibbertsen, P. An Overview of Modified Semiparametric Memory Estimation Methods. Econometrics 2018, 6, 13.

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