Non-redundancy of high order moment conditions for efficient GMM estimation of weak AR processes

Laurence Broze; Christian Francq; Jean Michel Zakoïan

doi:10.1016/S0165-1765(01)00387-1

Non-redundancy of high order moment conditions for efficient GMM estimation of weak AR processes

Université de Lille
Université du Littoral Côte d'Opale

Research output: Contribution to journal › Article › peer-review

Abstract

This paper considers GMM estimation of autoregressive processes. It is shown that, contrary to the case where the noise is independent [see Kim et al., Economics Letters 62 (1999) 265-270], using high-order moments can provide substantial efficiency gains for estimating the AR(p) model when the noise is only uncorrelated.

Original language	English
Pages (from-to)	317-322
Number of pages	6
Journal	Economics Letters
Volume	71
Issue number	3
DOIs	https://doi.org/10.1016/S0165-1765(01)00387-1
Publication status	Published - 1 Jan 2001
Externally published	Yes

Keywords

Autoregressive process
C13
C22
Efficiency gains
Empirical autocorrelations
GMM
Yule-Walker estimator

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10.1016/S0165-1765(01)00387-1

Cite this

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title = "Non-redundancy of high order moment conditions for efficient GMM estimation of weak AR processes",

abstract = "This paper considers GMM estimation of autoregressive processes. It is shown that, contrary to the case where the noise is independent [see Kim et al., Economics Letters 62 (1999) 265-270], using high-order moments can provide substantial efficiency gains for estimating the AR(p) model when the noise is only uncorrelated.",

keywords = "Autoregressive process, C13, C22, Efficiency gains, Empirical autocorrelations, GMM, Yule-Walker estimator",

author = "Laurence Broze and Christian Francq and Zako{\"i}an, \{Jean Michel\}",

year = "2001",

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doi = "10.1016/S0165-1765(01)00387-1",

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AU - Broze, Laurence

AU - Francq, Christian

AU - Zakoïan, Jean Michel

PY - 2001/1/1

Y1 - 2001/1/1

N2 - This paper considers GMM estimation of autoregressive processes. It is shown that, contrary to the case where the noise is independent [see Kim et al., Economics Letters 62 (1999) 265-270], using high-order moments can provide substantial efficiency gains for estimating the AR(p) model when the noise is only uncorrelated.

AB - This paper considers GMM estimation of autoregressive processes. It is shown that, contrary to the case where the noise is independent [see Kim et al., Economics Letters 62 (1999) 265-270], using high-order moments can provide substantial efficiency gains for estimating the AR(p) model when the noise is only uncorrelated.

KW - Autoregressive process

KW - C13

KW - C22

KW - Efficiency gains

KW - Empirical autocorrelations

KW - GMM

KW - Yule-Walker estimator

U2 - 10.1016/S0165-1765(01)00387-1

DO - 10.1016/S0165-1765(01)00387-1

M3 - Article

AN - SCOPUS:0035614562

SN - 0165-1765

VL - 71

SP - 317

EP - 322

JO - Economics Letters

JF - Economics Letters

IS - 3

ER -

Non-redundancy of high order moment conditions for efficient GMM estimation of weak AR processes

Abstract

Keywords

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