Stochastic Search Variable Selection Prior
Calculates the priors for a Bayesian VAR model, which employs stochastic search variable selection (SSVS).
ssvs_prior(object, tau = c(0.05, 10), semiautomatic = NULL)
object |
an object of class |
tau |
a numeric vector of two elements containing the prior standard errors of restricted
variables (τ_0) as its first element and unrestricted variables (τ_1)
as its second. Default is |
semiautomatic |
an optional numeric vector of two elements containing the factors by which the standard errors associated with an unconstrained least squares estimate of the VAR model are multiplied to obtain the prior standard errors of restricted (τ_0) and unrestricted (τ_1) variables. This is the semiautomatic approach described in George et al. (2008). |
A list containing the vectors of prior standard deviations for restricted and unrestricted variables, respectively.
George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553–580. doi: 10.1016/j.jeconom.2007.08.017
# Prepare data
data("e1")
data <- diff(log(e1))
# Generate model input
object <- gen_var(data)
# Obtain SSVS prior
prior <- ssvs_prior(object, semiautomatic = c(.1, 10))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.