MinimumEntropy_weights: Minimum entropy weights

In GediminasB/bayesVAR_TVP: Bayesian analysis of Vector Autoregressive Models

Description

Finds minimum entropy weights using Kullback-Leibler information criterion. New prediction density conditions are expressed by function g and vector g.rhs such that

Eg(pd) = g.rhs

Usage

MinimumEntropy_weights(pd, g, g.rhs)

Arguments

pd	Simulations of predictive density [T+1 x n x N.sim]
g	Transformation for pd used to express to new information. Must return vector of length p
g.rhs	Vector of length p

Value

pi.star	Adjusted probabilities
KLIC	Kullback-Leibler information criterion
gamma	Vector of Lagrange multipliers
optim.code	An integer indicating why the optimization process terminated. See ?nlm for info

References

• \insertRef

  Robertson2005bayesVAR

GediminasB/bayesVAR_TVP documentation built on Nov. 18, 2019, 6:44 p.m.