sim_mncoef {bvhar}

Generate Minnesota BVAR Parameters

Description

This function generates parameters of BVAR with Minnesota prior.

Usage

sim_mncoef(p, bayes_spec = set_bvar(), full = TRUE)

Arguments

p

VAR lag

bayes_spec

A BVAR model specification by set_bvar().

full

Generate variance matrix from IW (default: TRUE) or not (FALSE)?

Details

Implementing dummy observation constructions, Bańbura et al. (2010) sets Normal-IW prior.

A \mid \Sigma_e \sim MN(A_0, \Omega_0, \Sigma_e)

\Sigma_e \sim IW(S_0, \alpha_0)

If full = FALSE, the result of \Sigma_e is the same as input (diag(sigma)).

Value

List with the following component.

coefficients

BVAR coefficient (MN)

covmat

BVAR variance (IW or diagonal matrix of sigma of bayes_spec)

References

Bańbura, M., Giannone, D., & Reichlin, L. (2010). Large Bayesian vector auto regressions. Journal of Applied Econometrics, 25(1).

Karlsson, S. (2013). Chapter 15 Forecasting with Bayesian Vector Autoregression. Handbook of Economic Forecasting, 2, 791–897.

Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions: Five Years of Experience. Journal of Business & Economic Statistics, 4(1), 25.

See Also

• set_bvar() to specify the hyperparameters of Minnesota prior.

• bvar_adding_dummy for dummy observations definition.

Examples

# Generate (A, Sigma)
# BVAR(p = 2)
# sigma: 1, 1, 1
# lambda: .1
# delta: .1, .1, .1
# epsilon: 1e-04
set.seed(1)
sim_mncoef(
  p = 2,
  bayes_spec = set_bvar(
    sigma = rep(1, 3),
    lambda = .1,
    delta = rep(.1, 3),
    eps = 1e-04
  ),
  full = TRUE
)