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Bayesian Full Information Analysis of the Simultaneous Equations Model

  • J.-A. Morales
Part of the Lecture Notes in Operations Research and Mathematical Systems book series (LNE, volume 43)

Abstract

Let observations be generated by the following system of structural equations:
$$ By\left( t \right)\; + \;\Gamma z\left( t \right) = u\left( t \right) $$
(1.1)
where y(t) is an m dimensional vector of endogenous variables, z(t) is an n-dimensional vector of truly exogenous variables, B is an m × m non-singular matrix of coefficients with diagonal elements Bii = 1, i = 1,2,…,m, г is an m × n matrix of coefficients and u(t) is an m-dimensional vector of unobservable random disturbances with a non-degenerate joint distribution.1

Keywords

Posterior Density Marginal Density Prior Density Positive Definite Symmetric Matrix Simultaneous Equation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Drèze (10),(11).Google Scholar
  2. 1.
    Rothenberg (24)Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 1971

Authors and Affiliations

  • J.-A. Morales
    • 1
  1. 1.Center for Operations Research and EconometricsUniversité Catholique de LouvainBelgique

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