Asymptotic theory for maximum likelihood estimation of the memory parameter in stationary Gaussian processes

Offer Lieberman; Roy Rosemarin; Judith Rousseau

doi:https://doi.org/10.1017/S0266466611000399

Asymptotic theory for maximum likelihood estimation of the memory parameter in stationary Gaussian processes

Offer Lieberman, Roy Rosemarin, Judith Rousseau

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

Abstract

Consistency, asymptotic normality, and efficiency of the maximum likelihood estimator for stationary Gaussian time series were shown to hold in the short memory case by Hannan (1973, Journal of Applied Probability 10, 130-145) and in the long memory case by Dahlhaus (1989, Annals of Statistics 34, 1045-1047). In this paper we extend these results to the entire stationarity region, including the case of antipersistence and noninvertibility.

Original language	English
Pages (from-to)	457-470
Number of pages	14
Journal	Econometric Theory
Volume	28
Issue number	2
DOIs	https://doi.org/10.1017/S0266466611000399
State	Published - Apr 2012
Externally published	Yes

All Science Journal Classification (ASJC) codes

Social Sciences (miscellaneous)
Economics and Econometrics

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title = "Asymptotic theory for maximum likelihood estimation of the memory parameter in stationary Gaussian processes",

abstract = "Consistency, asymptotic normality, and efficiency of the maximum likelihood estimator for stationary Gaussian time series were shown to hold in the short memory case by Hannan (1973, Journal of Applied Probability 10, 130-145) and in the long memory case by Dahlhaus (1989, Annals of Statistics 34, 1045-1047). In this paper we extend these results to the entire stationarity region, including the case of antipersistence and noninvertibility.",

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doi = "https://doi.org/10.1017/S0266466611000399",

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N2 - Consistency, asymptotic normality, and efficiency of the maximum likelihood estimator for stationary Gaussian time series were shown to hold in the short memory case by Hannan (1973, Journal of Applied Probability 10, 130-145) and in the long memory case by Dahlhaus (1989, Annals of Statistics 34, 1045-1047). In this paper we extend these results to the entire stationarity region, including the case of antipersistence and noninvertibility.

AB - Consistency, asymptotic normality, and efficiency of the maximum likelihood estimator for stationary Gaussian time series were shown to hold in the short memory case by Hannan (1973, Journal of Applied Probability 10, 130-145) and in the long memory case by Dahlhaus (1989, Annals of Statistics 34, 1045-1047). In this paper we extend these results to the entire stationarity region, including the case of antipersistence and noninvertibility.

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Asymptotic theory for maximum likelihood estimation of the memory parameter in stationary Gaussian processes

Abstract

All Science Journal Classification (ASJC) codes

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