bvartools (version 0.2.4)

ssvs_prior: Stochastic Search Variable Selection Prior

Description

Calculates the priors for a Bayesian VAR model, which employs stochastic search variable selection (SSVS).

Usage

ssvs_prior(object, tau = c(0.05, 10), semiautomatic = NULL)

Value

A list containing the vectors of prior standard deviations for restricted and unrestricted variables, respectively.

Arguments

object

an object of class "bvarmodel", usually, a result of a call to gen_var or gen_vec.

tau

a numeric vector of two elements containing the prior standard errors of restricted variables (\(\tau_0\)) as its first element and unrestricted variables (\(\tau_1\)) as its second. Default is c(0.05, 10).

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 (\(\tau_0\)) and unrestricted (\(\tau_1\)) variables. This is the semiautomatic approach described in George et al. (2008).

References

George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553--580. tools:::Rd_expr_doi("10.1016/j.jeconom.2007.08.017")

Examples

# 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))