Beta: Bayesian estimate of VAR parameters

In gamalamboy/stresstest: Implement the ETI Stress Testing Simulation

Description

This function produces an estimated mean for the VAR parameters, based upon a) prior information, and b) the data matrices Y and Z. It is a weighted mean of the prior and the data estimate, using the precision matrices as weights.

Usage

Beta(prior_beta, prior_vbeta_inv, vbeta, var_inv, Y, Z)

Arguments

prior_beta	the prior parameter means.
prior_vbeta_inv	the prior precision matrix of the parameters.
vbeta	the updated covariance matrix of the parameters
var_inv	the updated precision matrix of the model
Y	a list of the model vectors for the dependent variable.
Z	the list of model matrices for t=1,…,τ.

Details

The model uses the following formula to generate the estimate of β:

V_{β} ( V_{β_{0} }^{-1} β_{0} + ∑_{t=1}^{τ} Z_{t}^{T} Σ^{-1} Z_{t} )

Value

An updated Bayesian estimate of the model parameters.

gamalamboy/stresstest documentation built on May 17, 2019, 1:33 p.m.