bvar_horseshoe {bvhar}

Fitting Bayesian VAR(p) of Horseshoe Prior

Description

This function fits BVAR(p) with horseshoe prior.

Usage

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, ...)

Arguments

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 set_horseshoe().

include_mean

Add constant term (Default: TRUE) or not (FALSE)

minnesota

Minnesota type

algo

Ordinary gibbs sampling ("gibbs") or blocked gibbs (Default: "block").

verbose

Print the progress bar in the console. By default, FALSE.

num_thread

Number of threads

x

bvarhs object

digits

digit option to print

...

not used

Value

bvar_horseshoe returns an object named bvarhs class. It is a list with the following components:

alpha_record

MCMC trace for vectorized coefficients (alpha \alpha) with posterior::draws_df format.

lambda_record

MCMC trace for local shrinkage level (lambda \lambda) with posterior::draws_df format.

tau_record

MCMC trace for global shrinkage level (tau \tau) with posterior::draws_df format.

psi_record

MCMC trace for precision matrix (psi \Psi) with list format.

chain

The numer of chains

coefficients

Posterior mean of VAR coefficients.

psi_posterior

Posterior mean of precision matrix \Psi

covmat

Posterior mean of covariance matrix

omega_record

MCMC trace for diagonal element of \Psi (omega) with posterior::draws_df format.

eta_record

MCMC trace for upper triangular element of \Psi (eta) with posterior::draws_df format.

param

posterior::draws_df with every variable: alpha, lambda, tau, omega, and eta

df

Numer of Coefficients: mp + 1 or mp

p

Lag of VAR

m

Dimension of the data

obs

Sample size used when training = totobs - p

totobs

Total number of the observation

call

Matched call

process

Description of the model, e.g. "VAR_Horseshoe"

type

include constant term ("const") or not ("none")

algo

Usual Gibbs sampling ("gibbs") or fast sampling ("fast")

spec

Horseshoe specification defined by set_horseshoe()

iter

Total iterations

burn

Burn-in

thin

Thinning

y0

Y_0

design

X_0

y

Raw input

References

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.