bvar_horseshoe {bvhar} | R Documentation |
This function fits BVAR(p) with horseshoe prior.
bvar_horseshoe(
y,
p,
num_chains = 1,
num_iter = 1000,
num_burn = floor(num_iter/2),
thinning = 1,
bayes_spec = set_horseshoe(),
include_mean = TRUE,
minnesota = FALSE,
algo = c("block", "gibbs"),
verbose = FALSE,
num_thread = 1
)
## S3 method for class 'bvarhs'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'bvarhs'
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 |
Horseshoe initialization specification by |
include_mean |
Add constant term (Default: |
minnesota |
Minnesota type |
algo |
Ordinary gibbs sampling ( |
verbose |
Print the progress bar in the console. By default, |
num_thread |
|
x |
|
digits |
digit option to print |
... |
not used |
bvar_horseshoe
returns an object named bvarhs
class.
It is a list with the following components:
Posterior mean of VAR coefficients.
Posterior mean of covariance matrix
Posterior mean of precision matrix \Psi
Posterior inclusion probabilities.
posterior::draws_df with every variable: alpha, lambda, tau, omega, and eta
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_Horseshoe
include constant term (const
) or not (none
)
Usual Gibbs sampling (gibbs
) or fast sampling (fast
)
Horseshoe specification defined by set_horseshoe()
The numer of chains
Total iterations
Burn-in
Thinning
Indicators for group.
Number of groups.
Y_0
X_0
Raw input
Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2), 465-480.
Makalic, E., & Schmidt, D. F. (2016). A Simple Sampler for the Horseshoe Estimator. IEEE Signal Processing Letters, 23(1), 179-182.