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Exact maximum likelihood estimation of structured or unit root multivariate time series models

  • Guy Melard
  • Roch Roy
  • Abdessamad Saidi
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    The exact likelihood function of a Gaussian vector autoregressive-moving average (VARMA) model is evaluated in two nonstandard cases: (a) a parsimonious structured form, such as obtained in the echelon form structure or the scalar component model (SCM) structure; (b) a partially nonstationary (integrated of order 1) model in error-correction form. The starting point is any algorithm for computing the exact likelihood of a Gaussian VARMA time series. Our algorithm also provides the parameter estimates and their standard errors. The small sample properties of our algorithm were studied by Monte Carlo methods. Examples with real data are provided. © 2005 Elsevier B.V. All rights reserved.

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    Paper provided by ULB -- Universite Libre de Bruxelles in its series ULB Institutional Repository with number 2013/13754.

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    Date of creation: 2006
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    Publication status: Published in: Computational Statistics & Data Analysis (2006)
    Handle: RePEc:ulb:ulbeco:2013/13754
    Note: SCOPUS: ar.j
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    Web page: http://difusion.ulb.ac.be

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