var_bayes {bvhar} | R Documentation |
Fitting Bayesian VAR with Coefficient and Covariance Prior
Description
This function fits BVAR.
Covariance term can be homoskedastic or heteroskedastic (stochastic volatility).
It can have Minnesota, SSVS, and Horseshoe prior.
Usage
var_bayes(
y,
p,
exogen = NULL,
s = 0,
num_chains = 1,
num_iter = 1000,
num_burn = floor(num_iter/2),
thinning = 1,
coef_spec = set_bvar(),
contem_spec = coef_spec,
cov_spec = set_ldlt(),
intercept = set_intercept(),
exogen_spec = coef_spec,
include_mean = TRUE,
minnesota = TRUE,
ggl = 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 'bvarldlt'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
## S3 method for class 'bvarsv'
knit_print(x, ...)
## S3 method for class 'bvarldlt'
knit_print(x, ...)
Arguments
y |
Time series data of which columns indicate the variables |
p |
VAR lag |
exogen |
Unmodeled variables |
s |
Lag of exogeneous variables in VARX(p, s). By default, |
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 |
coef_spec |
Coefficient prior specification by |
contem_spec |
Contemporaneous coefficient prior specification by |
cov_spec |
|
intercept |
|
exogen_spec |
Exogenous coefficient prior specification. |
include_mean |
Add constant term (Default: |
minnesota |
Apply cross-variable shrinkage structure (Minnesota-way). By default, |
ggl |
If |
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
var_bayes()
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.
Huber, F., Koop, G., & Onorante, L. (2021). Inducing Sparsity and Shrinkage in Time-Varying Parameter Models. Journal of Business & Economic Statistics, 39(3), 669-683.
Korobilis, D., & Shimizu, K. (2022). Bayesian Approaches to Shrinkage and Sparse Estimation. Foundations and Trends® in Econometrics, 11(4), 230-354.
Ray, P., & Bhattacharya, A. (2018). Signal Adaptive Variable Selector for the Horseshoe Prior. arXiv.