init_ssvs: Initial Parameters of Stochastic Search Variable Selection (SSVS) Model

Description

Set initial parameters before starting Gibbs sampler for SSVS.

Usage

init_ssvs(
  init_coef,
  init_coef_dummy,
  init_chol,
  init_chol_dummy,
  type = c("user", "auto")
)
# S3 method for ssvsinit
print(x, digits = max(3L, getOption("digits") - 3L), ...)
is.ssvsinit(x)
# S3 method for ssvsinit
knit_print(x, ...)

Value

ssvsinit object

Arguments

init_coef: Initial coefficient matrix. Initialize with an array or list for multiple chains.
init_coef_dummy: Initial indicator matrix (1-0) corresponding to each component of coefficient. Initialize with an array or list for multiple chains.
init_chol: Initial cholesky factor (upper triangular). Initialize with an array or list for multiple chains.
init_chol_dummy: Initial indicator matrix (1-0) corresponding to each component of cholesky factor. Initialize with an array or list for multiple chains.
type: Type to choose initial values. One of user (User-given) and auto (OLS for coefficients and 1 for dummy).
x: ssvsinit
digits: digit option to print
...: not used

Details

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.

References

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 Trends® in Econometrics, 3(4), 267-358.