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On Parameter Estimation for Exponential Dispersion Arma Models

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  • Peter X.‐K. Song
  • Dingan Feng

Abstract

. A class of autoregressive moving‐average (ARMA) models proposed by Jørgensen and Song [Journal of Applied Probability (1998), vol. 35, pp. 78–92] with exponential dispersion model margins are useful to deal with non‐normal stationary time series with high‐order autocorrelation. One property associated with the class of models is that the projection process takes the exact form of the classical Box and Jenkins ARMA representation, leading to considerable ease to establish theories. This paper focuses on the issue of parameter estimation for such models, which has not been thoroughly investigated in Jørgensen and Song's paper. The key of the proposed approach is to treat the residual process associated with the projection essentially as a measurement error, which enables us to formulate directly an ARMA representation for the observed time series. The parameter estimation therefore becomes straightforward using the existing methods for the Box and Jenkins ARMA models such as the quasi‐likelihood method. The approach is illustrated by simulation studies and by an analysis of myoclonic seizure counts.

Suggested Citation

  • Peter X.‐K. Song & Dingan Feng, 2005. "On Parameter Estimation for Exponential Dispersion Arma Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(6), pages 843-862, November.
    • Handle: RePEc:bla:jtsera:v:26:y:2005:i:6:p:843-862
    DOI: 10.1111/j.1467-9892.2005.00446.x
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    References listed on IDEAS

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      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
    2. Ashley, Richard & Vaughan, David, 1986. "Measuring Measurement Error in Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 95-103, January.
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