Description Usage Arguments Details Value
This function computes the scale matrix for the Wishart conditional posterior in the Independent Normal-Wishart Bayesian VAR Model.
1 |
beta |
the sampled mean for the N( β, V_{β}) distribution. It is updated for each run of the Gibbs sampler. |
prior_var |
the prior (k \times k) scale matrix for the model. |
Y |
a structure of class |
Z |
a structure of class |
This function uses the following formula for updating the previous value:
\hat{Σ} = (V_{0} + ∑_{t=1}^{τ}(Y - Zβ)(Y - Zβ)^{T} )^{-1}
Where V_{0} is the prior Scale Matrix, Y is a column vector of dependent variables, and Z are the Seemingly Unrelated Regression (SUR) representation of the lagged dependent variables and external regressors.
This function is not meant to be used directly by the user. Instead,
it is used by function Gibbs
in its recursive estimation.
An updated Bayesian estimate of the precision matrix of the Normal- Wishart prior used in the model.
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