bvar_ssvs {bvhar} | R Documentation |
This function fits BVAR(p) with stochastic search variable selection (SSVS) prior.
bvar_ssvs(
y,
p,
num_chains = 1,
num_iter = 1000,
num_burn = floor(num_iter/2),
thinning = 1,
bayes_spec = choose_ssvs(y = y, ord = p, type = "VAR", param = c(0.1, 10), include_mean
= include_mean, gamma_param = c(0.01, 0.01), mean_non = 0, sd_non = 0.1),
init_spec = init_ssvs(type = "auto"),
include_mean = TRUE,
minnesota = FALSE,
verbose = FALSE,
num_thread = 1
)
## S3 method for class 'bvarssvs'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'bvarssvs'
knit_print(x, ...)
y |
Time series data of which columns indicate the variables |
p |
VAR lag |
num_chains |
Number of MCMC chains |
num_iter |
MCMC iteration number |
num_burn |
Number of burn-in (warm-up). Half of the iteration is the default choice. |
thinning |
Thinning every thinning-th iteration |
bayes_spec |
A SSVS model specification by |
init_spec |
SSVS initialization specification by |
include_mean |
Add constant term (Default: |
minnesota |
Apply cross-variable shrinkage structure (Minnesota-way). By default, |
verbose |
Print the progress bar in the console. By default, |
num_thread |
|
x |
|
digits |
digit option to print |
... |
not used |
SSVS prior gives prior to parameters \alpha = vec(A)
(VAR coefficient) and \Sigma_e^{-1} = \Psi \Psi^T
(residual covariance).
\alpha_j \mid \gamma_j \sim (1 - \gamma_j) N(0, \kappa_{0j}^2) + \gamma_j N(0, \kappa_{1j}^2)
\gamma_j \sim Bernoulli(q_j)
and for upper triangular matrix \Psi
,
\psi_{jj}^2 \sim Gamma(shape = a_j, rate = b_j)
\psi_{ij} \mid w_{ij} \sim (1 - w_{ij}) N(0, \kappa_{0,ij}^2) + w_{ij} N(0, \kappa_{1,ij}^2)
w_{ij} \sim Bernoulli(q_{ij})
bvar_ssvs
returns an object named bvarssvs
class.
It is a list with the following components:
Posterior mean of VAR coefficients.
Posterior mean of cholesky factor matrix
Posterior mean of covariance matrix
Posterior mean of omega
Posterior inclusion probability
posterior::draws_df with every variable: alpha, eta, psi, omega, and gamma
Name of every parameter.
Numer of Coefficients: mp + 1
or mp
Lag of VAR
Dimension of the data
Sample size used when training = totobs
- p
Total number of the observation
Matched call
Description of the model, e.g. VAR_SSVS
include constant term (const
) or not (none
)
SSVS specification defined by set_ssvs()
Initial specification defined by init_ssvs()
The numer of chains
Total iterations
Burn-in
Thinning
Indicators for group.
Number of groups.
Y_0
X_0
Raw input
George, E. I., & McCulloch, R. E. (1993). Variable Selection via Gibbs Sampling. Journal of the American Statistical Association, 88(423), 881-889.
George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553-580.
Koop, G., & Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and TrendsĀ® in Econometrics, 3(4), 267-358.