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On the estimation of short memory components in long memory time series models

On the estimation of short memory components in long memory time series models AbstractA substantial amount of recent time series research has emphasized semi-parameteric estimators of a long memory parameter and we provide a selective review of the literature on this issue. We consider such estimators applied to the issue of estimating the parameters relating to a short memory process which is embedded within the long memory process. We consider the fractional differencing filter and the subsequent properties of a two step estimator of the short memory parameters. We conclude that while the semi-parametric estimators can have excellent properties in terms of estimating the long memory parameter, they do not have good properties when applied to the two step estimator of short memory I(0) parameters. In particular, these estimators compare poorly in terms of bias and mean squared error (MSE) with the systems based maximum likelihood estimator (MLE). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

On the estimation of short memory components in long memory time series models

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References (34)

Publisher
de Gruyter
Copyright
©2016 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1558-3708
eISSN
1558-3708
DOI
10.1515/snde-2015-0120
Publisher site
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Abstract

AbstractA substantial amount of recent time series research has emphasized semi-parameteric estimators of a long memory parameter and we provide a selective review of the literature on this issue. We consider such estimators applied to the issue of estimating the parameters relating to a short memory process which is embedded within the long memory process. We consider the fractional differencing filter and the subsequent properties of a two step estimator of the short memory parameters. We conclude that while the semi-parametric estimators can have excellent properties in terms of estimating the long memory parameter, they do not have good properties when applied to the two step estimator of short memory I(0) parameters. In particular, these estimators compare poorly in terms of bias and mean squared error (MSE) with the systems based maximum likelihood estimator (MLE).

Journal

Studies in Nonlinear Dynamics & Econometrics – de Gruyter

Published: Sep 1, 2016

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