ARMA modelling with non-Gaussian innovations

Wai Keung LI; A. I. MCLEOD

doi:10.1111/j.1467-9892.1988.tb00461.x

ARMA modelling with non-Gaussian innovations

Wai Keung LI
, A. I. MCLEOD

Department of Mathematics and Information Technology (MIT)

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

55 Citations (Scopus)

Abstract

The problem of modelling time series driven by non-Gaussian innovations is considered. The asymptotic normality of the maximum likelihood estimator is established under some general conditions. The distribution of the residual autocorrelations is also obtained. This gives rise to a potentially useful goodness-of-fit statistic. Applications of the results to two important cases are discussed. Two real examples are considered. Copyright © 1988 Wiley Blackwell. All rights reserved

Original language	English
Pages (from-to)	155-168
Journal	Journal of Time Series Analysis
Volume	9
Issue number	2
DOIs	https://doi.org/10.1111/j.1467-9892.1988.tb00461.x
Publication status	Published - Mar 1988

Keywords

Autoregressive moving-average process
Maximum likelihood estimation
Non-Gaussian innovations
Residual autocorrelations

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10.1111/j.1467-9892.1988.tb00461.x

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abstract = "The problem of modelling time series driven by non-Gaussian innovations is considered. The asymptotic normality of the maximum likelihood estimator is established under some general conditions. The distribution of the residual autocorrelations is also obtained. This gives rise to a potentially useful goodness-of-fit statistic. Applications of the results to two important cases are discussed. Two real examples are considered. Copyright {\textcopyright} 1988 Wiley Blackwell. All rights reserved",

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AB - The problem of modelling time series driven by non-Gaussian innovations is considered. The asymptotic normality of the maximum likelihood estimator is established under some general conditions. The distribution of the residual autocorrelations is also obtained. This gives rise to a potentially useful goodness-of-fit statistic. Applications of the results to two important cases are discussed. Two real examples are considered. Copyright © 1988 Wiley Blackwell. All rights reserved

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DO - 10.1111/j.1467-9892.1988.tb00461.x

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VL - 9

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JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

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ARMA modelling with non-Gaussian innovations

Abstract

Keywords

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