BVAR.SV: Bayesian inference of VAR model with RW-SV

In hoanguc3m/fatBVARS: Bayesian VAR with Stochastic volatility and fat tails

Bayesian inference of VAR model with RW-SV

Description

Bayesian inference of VAR model with RW-SV

y_t = B x_t + SQRT(w_t) A^(-1) H_t eps_t

Usage

BVAR.SV(y, K, p, dist, y0 = NULL, prior = NULL, inits = NULL)

Arguments

y	The input data as matrix T x K where T is the number of observations and K is the number of variables
K	The number of variables in BVAR model.
p	The number of lags in BVAR model.
dist	The variable specifies the BVAR error distribution. It should be one of c("Gaussian","Student","Skew.Student","Skew.Student", "MT","Skew.MT","MST").
y0	The number of observations
prior	The prior specification of BVAR.
inits	The initial values of BVAR.

Value

A coda object of the posterior samples.

Examples

## Not run: 
datagen <- sim.VAR.SV(dist="Gaussian")
y <- datagen$y
prior <- get_prior(y, p = 2, dist="Gaussian", SV = T)
inits <- get_init(prior)
Chain1 <- BVAR.SV(y, K = 5, p = 2, dist = "Gaussian", y0 = NULL, prior = prior, inits = inits)
plot(Chain1)

## End(Not run)

hoanguc3m/fatBVARS documentation built on Jan. 12, 2023, 4:42 p.m.