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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2002
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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 |
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Singapore Singapore |
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SMU Libraries |
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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 |