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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)

Abstract

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).

Keywords

Hessian Matrix Information Matrix Fu1l Column Rank Invertible Matrix Score Vector 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

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

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