bvar_flat {bvhar} | R Documentation |
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 |
include_mean |
Add constant term (Default: |
verbose |
Print the progress bar in the console. By default, |
num_thread |
Number of threads |
x |
Any object |
digits |
digit option to print |
... |
not used |
object |
A |
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()
, andfitted.bvarflat()
-
predict.bvarflat()
to forecast the BVHAR process