bvar_sv {bvhar} | R Documentation |
Fitting Bayesian VAR-SV
Description
This function fits VAR-SV.
It can have Minnesota, SSVS, and Horseshoe prior.
Usage
bvar_sv(
y,
p,
num_chains = 1,
num_iter = 1000,
num_burn = floor(num_iter/2),
thinning = 1,
bayes_spec = set_bvar(),
sv_spec = set_sv(),
intercept = set_intercept(),
include_mean = TRUE,
minnesota = TRUE,
save_init = FALSE,
convergence = NULL,
verbose = FALSE,
num_thread = 1
)
## S3 method for class 'bvarsv'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'bvarsv'
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 |
sv_spec |
|
intercept |
|
include_mean |
Add constant term (Default: |
minnesota |
Apply cross-variable shrinkage structure (Minnesota-way). By default, |
save_init |
Save every record starting from the initial values ( |
convergence |
Convergence threshold for rhat < convergence. By default, |
verbose |
Print the progress bar in the console. By default, |
num_thread |
Number of threads |
x |
|
digits |
digit option to print |
... |
not used |
Details
Cholesky stochastic volatility modeling for VAR based on
\Sigma_t^{-1} = L^T D_t^{-1} L
, and implements corrected triangular algorithm for Gibbs sampler.
Value
bvar_sv()
returns an object named bvarsv
class.
- coefficients
Posterior mean of coefficients.
- chol_posterior
Posterior mean of contemporaneous effects.
- param
Every set of MCMC trace.
- param_names
Name of every parameter.
- group
Indicators for group.
- num_group
Number of groups.
- df
Numer of Coefficients:
3m + 1
or3m
- p
VAR lag
- 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.
VHAR_SSVS_SV
,VHAR_Horseshoe_SV
, orVHAR_minnesota-part_SV
- type
include constant term (
const
) or not (none
)- spec
Coefficients prior specification
- sv
log volatility prior specification
- intercept
Intercept prior specification
- init
Initial values
- chain
The numer of chains
- iter
Total iterations
- burn
Burn-in
- thin
Thinning
- y0
Y_0
- design
X_0
- y
Raw input
If it is SSVS or Horseshoe:
- pip
Posterior inclusion probabilities.
References
Carriero, A., Chan, J., Clark, T. E., & Marcellino, M. (2022). Corrigendum to “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors” [J. Econometrics 212 (1)(2019) 137-154]. Journal of Econometrics, 227(2), 506-512.
Chan, J., Koop, G., Poirier, D., & Tobias, J. (2019). Bayesian Econometric Methods (2nd ed., Econometric Exercises). Cambridge: Cambridge University Press.
Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302.
Gruber, L., & Kastner, G. (2022). Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! arXiv.