bvhar_horseshoe: Fitting Bayesian VHAR of Horseshoe Prior

Description

This function fits VHAR with horseshoe prior.

Usage

bvhar_horseshoe(
  y,
  har = c(5, 22),
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  bayes_spec = set_horseshoe(),
  include_mean = TRUE,
  minnesota = c("no", "short", "longrun"),
  algo = c("block", "gibbs"),
  verbose = FALSE,
  num_thread = 1
)
# S3 method for bvharhs
print(x, digits = max(3L, getOption("digits") - 3L), ...)
# S3 method for bvharhs
knit_print(x, ...)

Value

bvhar_horseshoe returns an object named bvarhs

class. It is a list with the following components:

coefficients: Posterior mean of VHAR coefficients.

covmat

Posterior mean of covariance matrix

psi_posterior

Posterior mean of precision matrix \(\Psi\)

param

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

param_names

Name of every parameter.

Numer of Coefficients: 3m + 1 or 3m

3 (The number of terms. It contains this element for usage in other functions.)

week

Order for weekly term

month

Order for monthly term

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. VHAR_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()

chain

The numer of chains

iter

Total iterations

burn

Burn-in

thin

Thinning

group

Indicators for group.

num_group

Number of groups.

HARtrans

VHAR linear transformation matrix

\(Y_0\)

design

\(X_0\)

Raw input

Arguments

y: Time series data of which columns indicate the variables
har: Numeric vector for weekly and monthly order. By default, c(5, 22).
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: bvharhs object
digits: digit option to print
...: not used

References

Kim, Y. G., and Baek, C. (n.d.). Working paper.