Efficient estimation of seasonal long-range-dependent processes

Wilfredo Palma; Ngai Hang Chan

doi:10.1111/j.1467-9892.2005.00447.x

Efficient estimation of seasonal long-range-dependent processes

Wilfredo Palma, Ngai Hang Chan

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

21 Citations (Scopus)

Abstract

This paper studies asymptotic properties of the exact maximum likelihood estimates (MLE) for a general class of Gaussian seasonal long-range-dependent processes. This class includes the commonly used Gegenbauer and seasonal autoregressive fractionally integrated moving average processes. By means of an approximation of the spectral density, the exact MLE of this class are shown to be consistent, asymptotically normal and efficient. Finite sample performance of these estimates is examined by Monte Carlo simulations and it is shown that the estimates behave very well even for moderate sample sizes. The estimation methodology is illustrated by a real-life Internet traffic example. © 2005 Blackwell Publishing Ltd.

Original language	English
Pages (from-to)	863-892
Journal	Journal of Time Series Analysis
Volume	26
Issue number	6
DOIs	https://doi.org/10.1111/j.1467-9892.2005.00447.x
Publication status	Published - Nov 2005
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Consistency
Cyclical
Efficiency
Long-range dependency
Maximum likelihood estimates (MLE)

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10.1111/j.1467-9892.2005.00447.x

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title = "Efficient estimation of seasonal long-range-dependent processes",

abstract = "This paper studies asymptotic properties of the exact maximum likelihood estimates (MLE) for a general class of Gaussian seasonal long-range-dependent processes. This class includes the commonly used Gegenbauer and seasonal autoregressive fractionally integrated moving average processes. By means of an approximation of the spectral density, the exact MLE of this class are shown to be consistent, asymptotically normal and efficient. Finite sample performance of these estimates is examined by Monte Carlo simulations and it is shown that the estimates behave very well even for moderate sample sizes. The estimation methodology is illustrated by a real-life Internet traffic example. {\textcopyright} 2005 Blackwell Publishing Ltd.",

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T1 - Efficient estimation of seasonal long-range-dependent processes

AU - Palma, Wilfredo

AU - Chan, Ngai Hang

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2005/11

Y1 - 2005/11

N2 - This paper studies asymptotic properties of the exact maximum likelihood estimates (MLE) for a general class of Gaussian seasonal long-range-dependent processes. This class includes the commonly used Gegenbauer and seasonal autoregressive fractionally integrated moving average processes. By means of an approximation of the spectral density, the exact MLE of this class are shown to be consistent, asymptotically normal and efficient. Finite sample performance of these estimates is examined by Monte Carlo simulations and it is shown that the estimates behave very well even for moderate sample sizes. The estimation methodology is illustrated by a real-life Internet traffic example. © 2005 Blackwell Publishing Ltd.

AB - This paper studies asymptotic properties of the exact maximum likelihood estimates (MLE) for a general class of Gaussian seasonal long-range-dependent processes. This class includes the commonly used Gegenbauer and seasonal autoregressive fractionally integrated moving average processes. By means of an approximation of the spectral density, the exact MLE of this class are shown to be consistent, asymptotically normal and efficient. Finite sample performance of these estimates is examined by Monte Carlo simulations and it is shown that the estimates behave very well even for moderate sample sizes. The estimation methodology is illustrated by a real-life Internet traffic example. © 2005 Blackwell Publishing Ltd.

KW - Consistency

KW - Cyclical

KW - Efficiency

KW - Long-range dependency

KW - Maximum likelihood estimates (MLE)

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-27644546903&origin=recordpage

U2 - 10.1111/j.1467-9892.2005.00447.x

DO - 10.1111/j.1467-9892.2005.00447.x

M3 - RGC 21 - Publication in refereed journal

SN - 0143-9782

VL - 26

SP - 863

EP - 892

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

IS - 6

ER -

Efficient estimation of seasonal long-range-dependent processes

Abstract

Bibliographical note

Research Keywords

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