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Dynamic Error-in-Variable Models and Limited Information Analysis

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  • Jean-Pierre Florens
  • Michel Mouchart
  • Jean-François Richard

Abstract

A vector stochastic process may be decomposed in to its expectation and a residual process. A linear dynamic model is defined by a set of dynamic linear relations constraining the 's given some conditioning variables and by the distribution of the process. This paper presents a strategy for the specification of this class of models providing computable posterior distributions for a suitable class of prior measures. Some conditional independence properties characterizing exogeneity conditions through global or sequential cuts, innovation property or non causality relations are studied and are shown to allow reductions by conditioning of the model.

Suggested Citation

  • Jean-Pierre Florens & Michel Mouchart & Jean-François Richard, 1987. "Dynamic Error-in-Variable Models and Limited Information Analysis," Annals of Economics and Statistics, GENES, issue 6-7, pages 289-310.
    • Handle: RePEc:adr:anecst:y:1987:i:6-7:p:289-310
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    File URL: http://www.jstor.org/stable/20075658
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    Cited by:

    1. Steel, Mark F. J. & Richard, Jean-Francois, 1991. "Bayesian multivariate exogeneity analysis : An application to a UK money demand equation," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 239-274.

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