set_ldlt: Covariance Matrix Prior Specification

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

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 class '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.

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