Tackling and Understanding the Small Sample Bias in ARFIMA Models

Posted: 14 Sep 1999

See all articles by Rolf Tschernig

Rolf Tschernig

University of Regensburg - Department of Economics and Econometrics; Maastricht University - Department of Quantitative Economics

Date Written: August 1994

Abstract

It is shown that the approximate Whittle estimator suffers from an inherent small sample estimation bias which is not present in other frequency domain maximum likelihood methods. A bias corrected approximate Whittle estimator is derived and compared with existing alternatives. Furthermore, the behavior of bias in ARFIMA (p, d, q) models is addressed. The results of a systematic Monte Carlo study suggest that the size of the estimation bias and of the mean squared error is related to the asymptotic parameter correlation evaluated at the true parameters. Among other things, this implies the observed independence of bias and mean squared error from the memory parameter.

JEL Classification: G00

Suggested Citation

Tschernig, Rolf, Tackling and Understanding the Small Sample Bias in ARFIMA Models (August 1994). Available at SSRN: https://ssrn.com/abstract=5652

Rolf Tschernig (Contact Author)

University of Regensburg - Department of Economics and Econometrics ( email )

Universitaetsstrasse 31
D-93040 Regensburg
Germany
+49 (0) 941 943 2737 (Phone)
+49 (0) 941 943 4917 (Fax)

HOME PAGE: www.wiwi.uni-regensburg.de/tschernig

Maastricht University - Department of Quantitative Economics ( email )

P.O. Box 616
Maastricht, 6200 MD
Netherlands

HOME PAGE: www.personeel.unimaas.nl/r.tschernig

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