sim_mncoef: Generate Minnesota BVAR Parameters
In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

sim_mncoef

R Documentation

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.

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
)

bvhar documentation built on April 4, 2025, 5:22 a.m.