choose_ssvs {bvhar}

Choose the Hyperparameters Set of SSVS-VAR using a Default Semiautomatic Approach

Description

This function chooses (\tau_{0i}, \tau_{1i}) and (\kappa_{0i}, \kappa_{1i}) using a default semiautomatic approach.

Usage

choose_ssvs(
  y,
  ord,
  type = c("VAR", "VHAR"),
  param = c(0.1, 10),
  include_mean = TRUE,
  gamma_param = c(0.01, 0.01),
  mean_non = 0,
  sd_non = 0.1
)

Arguments

y

Time series data of which columns indicate the variables.

ord

Order for VAR or VHAR.

type

Model type (Default: "VAR" or "VHAR").

param

Preselected constants c_0 << c_1. By default, 0.1 and 10 (See Details).

include_mean

Add constant term (Default: TRUE) or not (FALSE).

gamma_param

Parameters (shape, rate) for Gamma distribution. This is for the output.

mean_non

Prior mean of unrestricted coefficients. This is for the output.

sd_non

Standard deviance of unrestricted coefficients. This is for the output.

Details

Instead of using subjective values of (\tau_{0i}, \tau_{1i}), we can use

\tau_{ki} = c_k \hat{VAR(OLS)}

It must be c_0 << c_1.

In case of (\omega_{0ij}, \omega_{1ij}),

\omega_{kij} = c_k = \hat{VAR(OLS)}

similarly.

Value

ssvsinput object

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.