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Estimation of vector ARMAX models

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  • Hannan, E. J.
  • Dunsmuir, W. T. M.
  • Deistler, M.
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    Abstract

    The asymptotic properties of maximum likelihood estimates of a vector ARMAX system are considered under general conditions, relating to the nature of the exogenous variables and the innovation sequence and to the form of the parameterization of the rational transfer functions, from exogenous variables and innovations to the output vector. The exogenous variables are assumed to be such that the sample serial covariances converge to limits. The innovations are assumed to be martingale differences and to be nondeterministic in a fairly weak sense. Stronger conditions ensure that the asymptotic distribution of the estimates has the same covariance matrix as for Gaussian innovations but these stronger conditions are somewhat implausible. With each ARMAX structure may be associated an integer (the McMillan degree) and all structures for a given value of this integer may be topologised as an analytic manifold. Other parameterizations and topologisations of spaces of structures as analytic manifolds may also be considered and the presentation is sufficiently general to cover a wide range of these. Greater generality is also achieved by allowing for general forms of constraints.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 10 (1980)
    Issue (Month): 3 (September)
    Pages: 275-295

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    Handle: RePEc:eee:jmvana:v:10:y:1980:i:3:p:275-295

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    Related research

    Keywords: ARMAX systems strong law central limit theorem martingale Kronecker invariants dynamical indices McMillan degree analytic manifold;

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    Cited by:
    1. Boubacar Mainassara, Yacouba & Francq, Christian, 2009. "Estimating structural VARMA models with uncorrelated but non-independent error terms," MPRA Paper 15141, University Library of Munich, Germany.
    2. Pierre Duchesne, 2005. "On the asymptotic distribution of residual autocovariances in VARX models with applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 14(2), pages 449-473, December.
    3. Findley, David F. & Potscher, Benedikt M. & Wei, Ching-Zong, 2004. "Modeling of time series arrays by multistep prediction or likelihood methods," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 151-187.
    4. Peter M. Robinson & J. Vidal Sanz, 2005. "Modified whittle estimation of multilateral models on a lattice," LSE Research Online Documents on Economics 4545, London School of Economics and Political Science, LSE Library.
    5. Peter Robinson & J. Vidal Sanz, 2003. "Modified whittle estimation of multilateral spatial models," CeMMAP working papers CWP18/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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