init_ssvs {bvhar} | R Documentation |
Set initial parameters before starting Gibbs sampler for SSVS.
init_ssvs(
init_coef,
init_coef_dummy,
init_chol,
init_chol_dummy,
type = c("user", "auto")
)
## S3 method for class 'ssvsinit'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
is.ssvsinit(x)
## S3 method for class 'ssvsinit'
knit_print(x, ...)
Set SSVS initialization for the VAR model.
init_coef
: (kp + 1) x m A
coefficient matrix.
init_coef_dummy
: kp x m \Gamma
dummy matrix to restrict the coefficients.
init_chol
: k x k \Psi
upper triangular cholesky factor, which \Psi \Psi^\intercal = \Sigma_e^{-1}
.
init_chol_dummy
: k x k \Omega
upper triangular dummy matrix to restrict the cholesky factor.
Denote that init_chol
and init_chol_dummy
should be upper_triangular or the function gives error.
For parallel chain initialization, assign three-dimensional array or three-length list.
ssvsinit
object
George, E. I., & McCulloch, R. E. (1993). Variable Selection via Gibbs Sampling. Journal of the American Statistical Association, 88(423), 881-889.
George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553-580.
Koop, G., & Korobilis, D. (2009). Bayesian Multivariate Time Series Methods for Empirical Macroeconomics. Foundations and TrendsĀ® in Econometrics, 3(4), 267-358.