bvar_flat: Fitting Bayesian VAR(p) of Flat Prior

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Fitting Bayesian VAR(p) of Flat Prior

Description

This function fits BVAR(p) with flat prior.

Usage

bvar_flat(
  y,
  p,
  num_chains = 1,
  num_iter = 1000,
  num_burn = floor(num_iter/2),
  thinning = 1,
  bayes_spec = set_bvar_flat(),
  include_mean = TRUE,
  verbose = FALSE,
  num_thread = 1
)

## S3 method for class 'bvarflat'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

## S3 method for class 'bvarflat'
logLik(object, ...)

## S3 method for class 'bvarflat'
AIC(object, ...)

## S3 method for class 'bvarflat'
BIC(object, ...)

is.bvarflat(x)

## S3 method for class 'bvarflat'
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	A BVAR model specification by set_bvar_flat().
include_mean	Add constant term (Default: TRUE) or not (FALSE)
verbose	Print the progress bar in the console. By default, FALSE.
num_thread	Number of threads
x	Any object
digits	digit option to print
...	not used
object	A bvarflat object

Details

Ghosh et al. (2018) gives flat prior for residual matrix in BVAR.

Under this setting, there are many models such as hierarchical or non-hierarchical. This function chooses the most simple non-hierarchical matrix normal prior in Section 3.1.

A \mid \Sigma_e \sim MN(0, U^{-1}, \Sigma_e)

where U: precision matrix (MN: matrix normal).

p (\Sigma_e) \propto 1

Value

bvar_flat() returns an object bvarflat class. It is a list with the following components:

coefficients

Posterior Mean matrix of Matrix Normal distribution

fitted.values

Fitted values

residuals

Residuals

mn_prec

Posterior precision matrix of Matrix Normal distribution

iw_scale

Posterior scale matrix of posterior inverse-wishart distribution

iw_shape

Posterior shape of inverse-wishart distribution

df

Numer of Coefficients: mp + 1 or mp

p

Lag of VAR

m

Dimension of the time series

obs

Sample size used when training = totobs - p

totobs

Total number of the observation

process

Process string in the bayes_spec: BVAR_Flat

spec

Model specification (bvharspec)

type

include constant term (const) or not (none)

call

Matched call

prior_mean

Prior mean matrix of Matrix Normal distribution: zero matrix

prior_precision

Prior precision matrix of Matrix Normal distribution: U^{-1}

y0

Y_0

design

X_0

y

Raw input (matrix)

References

Ghosh, S., Khare, K., & Michailidis, G. (2018). High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models. Journal of the American Statistical Association, 114(526).

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

See Also

• set_bvar_flat() to specify the hyperparameters of BVAR flat prior.

• coef.bvarflat(), residuals.bvarflat(), and fitted.bvarflat()

• predict.bvarflat() to forecast the BVHAR process

bvhar documentation built on June 25, 2025, 9:07 a.m.