Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets

In this paper, we examine the finite-sample properties of the approximate maximum likelihood estimate (MLE) of the fractional differencing parameter d in an ARFIMA(p, d, q) model based on the wavelet coefficients. Ignoring wavelet coefficients of higher order of resolution, the remaining wavelet coe...

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Main Authors: TSE, Yiu Kuen, Anh, V. V., Tieng, Q. M.
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語言:English
出版: Institutional Knowledge at Singapore Management University 2002
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spelling sg-smu-ink.soe_research-13202010-09-23T05:48:03Z Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets TSE, Yiu Kuen Anh, V. V. Tieng, Q. M. In this paper, we examine the finite-sample properties of the approximate maximum likelihood estimate (MLE) of the fractional differencing parameter d in an ARFIMA(p, d, q) model based on the wavelet coefficients. Ignoring wavelet coefficients of higher order of resolution, the remaining wavelet coefficients approximate a sample of independently and identically distributed normal variates with homogeneous variance within each level. The approximate MLE performs satisfactorily and provides a robust estimate for which the short memory component need not be specified. 2002-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/321 info:doi/10.1016/s0378-4754(01)00403-7 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Econometrics
spellingShingle Econometrics
TSE, Yiu Kuen
Anh, V. V.
Tieng, Q. M.
Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets
description In this paper, we examine the finite-sample properties of the approximate maximum likelihood estimate (MLE) of the fractional differencing parameter d in an ARFIMA(p, d, q) model based on the wavelet coefficients. Ignoring wavelet coefficients of higher order of resolution, the remaining wavelet coefficients approximate a sample of independently and identically distributed normal variates with homogeneous variance within each level. The approximate MLE performs satisfactorily and provides a robust estimate for which the short memory component need not be specified.
format text
author TSE, Yiu Kuen
Anh, V. V.
Tieng, Q. M.
author_facet TSE, Yiu Kuen
Anh, V. V.
Tieng, Q. M.
author_sort TSE, Yiu Kuen
title Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets
title_short Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets
title_full Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets
title_fullStr Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets
title_full_unstemmed Maximum Likelihood Estimation of the Fractional Differencing Parameter in an Arfima Model Using Wavelets
title_sort maximum likelihood estimation of the fractional differencing parameter in an arfima model using wavelets
publisher Institutional Knowledge at Singapore Management University
publishDate 2002
url https://ink.library.smu.edu.sg/soe_research/321
_version_ 1770569115041267712