bvhar (version 2.2.2)

set_ldlt: Covariance Matrix Prior Specification

Description

Set prior for covariance matrix.

Usage

set_ldlt(ig_shape = 3, ig_scl = 0.01)
set_sv(ig_shape = 3, ig_scl = 0.01, initial_mean = 1, initial_prec = 0.1)
# S3 method for covspec
print(x, digits = max(3L, getOption("digits") - 3L), ...)
is.covspec(x)
is.svspec(x)
is.ldltspec(x)

Arguments

ig_shape

Inverse-Gamma shape of Cholesky diagonal vector. For SV (set_sv()), this is for state variance.

ig_scl

Inverse-Gamma scale of Cholesky diagonal vector. For SV (set_sv()), this is for state variance.

initial_mean

Prior mean of initial state.

initial_prec

Prior precision of initial state.

x

Any object

digits

digit option to print

...

not used

Details

set_ldlt() specifies LDLT of precision matrix, $$\Sigma^{-1} = L^T D^{-1} L$$

set_sv() specifices time varying precision matrix under stochastic volatility framework based on $$\Sigma_t^{-1} = L^T D_t^{-1} L$$

References

Carriero, A., Chan, J., Clark, T. E., & Marcellino, M. (2022). Corrigendum to “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors” [J. Econometrics 212 (1)(2019) 137-154]. Journal of Econometrics, 227(2), 506-512.

Chan, J., Koop, G., Poirier, D., & Tobias, J. (2019). Bayesian Econometric Methods (2nd ed., Econometric Exercises). Cambridge: Cambridge University Press.