bvar: Estimate six types of bayesian VAR models

In bdemeshev/bvarrKK: Translation Of Koop And Korobilis BVAR Matlab Code Into R

Description

Estimate six types of bayesian VAR models

Usage

bvar(Yraw, prior = c("diffuse", "minnesota", "conjugate", "independent",
  "ssvs-wishart", "ssvs-ssvs"), W = NULL, p = 4, constant = TRUE,
  nsave = 10000, nburn = 2000, it_print = 2000, impulses = TRUE,
  ihor = 24, forecasting = TRUE, repfor = 50, h = 1, p_i = 0.5,
  q_ij = 0.5, kappa_0 = 0.1, kappa_1 = 6, a_i = 0.01, b_i = 0.01,
  a_bar = c(0.5, 0.5, 10^2))

Arguments

Yraw	the matrix or data.frame with endogeneous VAR variables
prior	the type of prior: 'diffuse','minnesota','conjugate', 'independent','ssvs-Wishart','ssvs-ssvs'
W	the matrix of exogenous variables
p	the number of lags for endogeneous variables
constant	(TRUE/FALSE) indicator, whether the constant is included
nsave	the length of mcmc chain
nburn	the length of burn-in of mcmc chain
it_print	which iteration are printed
impulses	whether to calculate irfs
ihor	the number of lags for irfs
forecasting	whether to calculate forecasts
repfor	number of times to obtain a draw from the predictive
h	number of forecast periods
p_i	ssvs-ssvs hyperparameter, for Gamma ~ BERNOULLI(m,p_i), see eq. (14)
q_ij	ssvs-ssvs hyperparameter, for Omega_[j] ~ BERNOULLI(j,q_ij), see eq. (17)
kappa_0	ssvs-ssvs hyperparameter, variances for non-diagonal elements of SIGMA
kappa_1	ssvs-ssvs hyperparameter, variances for non-diagonal elements of SIGMA
a_i	ssvs-ssvs hyperparameter, diagonal elements of SIGMA (Gamma density)
b_i	ssvs-ssvs hyperparameter, diagonal elements of SIGMA (Gamma density)
a_bar	minnesota hyperparameter vector (own lag, other lag, exogeneuos)

Details

Estimate six types of bayesian VAR models

Value

the list containing all results of bayesian VAR estimation

Examples

data(Yraw)
bvar(Yraw, nsave = 1000, nburn = 100)

bdemeshev/bvarrKK documentation built on May 12, 2019, 3:40 a.m.