Large sample properties of parameter least squares estimates for time-varying ARMA models

Christian Francq; Antony Gautier

doi:10.1111/j.1467-9892.2004.02003.x

Large sample properties of parameter least squares estimates for time-varying ARMA models

Christian Francq, Antony Gautier

Université de Lille

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

Abstract

This paper considers estimation of ARMA models with time-varying coefficients. The ARMA parameters belong to d different regimes. The changes in regime occur at irregular time intervals. Consistency and asymptotic normality of least squares and quasi-generalized least squares estimators are shown.

Original language	English
Pages (from-to)	765-783
Number of pages	19
Journal	Journal of Time Series Analysis
Volume	25
Issue number	5
DOIs	https://doi.org/10.1111/j.1467-9892.2004.02003.x
Publication status	Published - 1 Sept 2004
Externally published	Yes

Keywords

Asymptotic normality
Consistency
Non-stationary processes
Quasi-generalized least squares estimator
Time-varying models

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10.1111/j.1467-9892.2004.02003.x

Cite this

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title = "Large sample properties of parameter least squares estimates for time-varying ARMA models",

abstract = "This paper considers estimation of ARMA models with time-varying coefficients. The ARMA parameters belong to d different regimes. The changes in regime occur at irregular time intervals. Consistency and asymptotic normality of least squares and quasi-generalized least squares estimators are shown.",

keywords = "Asymptotic normality, Consistency, Non-stationary processes, Quasi-generalized least squares estimator, Time-varying models",

author = "Christian Francq and Antony Gautier",

year = "2004",

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doi = "10.1111/j.1467-9892.2004.02003.x",

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AU - Gautier, Antony

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N2 - This paper considers estimation of ARMA models with time-varying coefficients. The ARMA parameters belong to d different regimes. The changes in regime occur at irregular time intervals. Consistency and asymptotic normality of least squares and quasi-generalized least squares estimators are shown.

AB - This paper considers estimation of ARMA models with time-varying coefficients. The ARMA parameters belong to d different regimes. The changes in regime occur at irregular time intervals. Consistency and asymptotic normality of least squares and quasi-generalized least squares estimators are shown.

KW - Asymptotic normality

KW - Consistency

KW - Non-stationary processes

KW - Quasi-generalized least squares estimator

KW - Time-varying models

U2 - 10.1111/j.1467-9892.2004.02003.x

DO - 10.1111/j.1467-9892.2004.02003.x

M3 - Article

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VL - 25

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EP - 783

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

IS - 5

ER -

Large sample properties of parameter least squares estimates for time-varying ARMA models

Abstract

Keywords

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