Impact of the periodicity and trend on the FD parameter estimation

A Almasri; B Holmquist; Shakir Hussain

doi:10.1080/10629360600570863

Impact of the periodicity and trend on the FD parameter estimation

A Almasri
, B Holmquist
, Shakir Hussain

Research output: Contribution to journal › Article

1 Citation (Scopus)

Abstract

It is well known that one of the features of long-memory processes is that they tend to have what looks like trends and cycles. A consequence of this property is that it is difficult to distinguish a long-memory process from a nonstationary process. In this paper, we study the impact of the periodicity and trend on different methods for estimating the long-memory processes parameter d.

Original language	English
Pages (from-to)	79-87
Number of pages	9
Journal	Journal of Statistical Computation and Simulation
Volume	77
Issue number	1
DOIs	https://doi.org/10.1080/10629360600570863
Publication status	Published - 1 Jan 2007

Keywords

trend
periodicity
long-memory
discrete wavelet transform

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10.1080/10629360600570863

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abstract = "It is well known that one of the features of long-memory processes is that they tend to have what looks like trends and cycles. A consequence of this property is that it is difficult to distinguish a long-memory process from a nonstationary process. In this paper, we study the impact of the periodicity and trend on different methods for estimating the long-memory processes parameter d.",

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AU - Holmquist, B

AU - Hussain, Shakir

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N2 - It is well known that one of the features of long-memory processes is that they tend to have what looks like trends and cycles. A consequence of this property is that it is difficult to distinguish a long-memory process from a nonstationary process. In this paper, we study the impact of the periodicity and trend on different methods for estimating the long-memory processes parameter d.

AB - It is well known that one of the features of long-memory processes is that they tend to have what looks like trends and cycles. A consequence of this property is that it is difficult to distinguish a long-memory process from a nonstationary process. In this paper, we study the impact of the periodicity and trend on different methods for estimating the long-memory processes parameter d.

KW - trend

KW - periodicity

KW - long-memory

KW - discrete wavelet transform

U2 - 10.1080/10629360600570863

DO - 10.1080/10629360600570863

M3 - Article

VL - 77

SP - 79

EP - 87

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

IS - 1

ER -

Impact of the periodicity and trend on the FD parameter estimation

Abstract

Keywords

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