Identification Analysis and F.I.M.L. Estimation for the K-Model

  • Carlo Giannini
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 381)


The K-model1 is completely defined by the following equations and distributional assumptions:
$$ K\;{\varepsilon _t} = {e_t} $$
$$ E({e_t}) = [0]\quad E({e_t}e'{}_t) = {I_n} $$
$$ {\varepsilon _t} \sim IMN([0],\sum )\quad \det (\sum ) \ne 0 $$
(ε t , the vector of the VAR model disturbances A(L) y t = ε t , is a Gaussian vector white noise, i.e. a vector of independent multivariate normally distributed variables with an associated positive definite variance-covariance matrix).


Covariance Assure Avar 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Carlo Giannini
    • 1
  1. 1.Department of EconomicsUniversity of AnconaAnconaItaly

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