| estimate {bsvars} | R Documentation |
Bayesian estimation of Structural Vector Autoregressions via Gibbs sampler
Description
Estimates homo- or heteroskedastic SVAR models using the Gibbs sampler proposed by Waggoner & Zha (2003)
for the structural matrix B and the equation-by-equation sampler by Chan, Koop, & Yu (2021)
for the autoregressive slope parameters A. Additionally, the parameter matrices A and B
follow a Minnesota prior and generalised-normal prior distributions respectively with the matrix-specific
overall shrinkage parameters estimated using a hierarchical prior distribution. A variety of models
for conditional variances are possible including versions of Stochastic Volatility and Markov-switching heteroskedasticity.
Non-normal specifications include finite and sparse normal mixture model for the structural shocks.
See section Details for the model equations.
Usage
estimate(specification, S, thin = 10, show_progress = TRUE)
Arguments
specification |
an object of class BSVAR, BSVARMSH, BSVARMIX, or BSVARSV generated using one of the |
S |
a positive integer, the number of posterior draws to be generated |
thin |
a positive integer, specifying the frequency of MCMC output thinning |
show_progress |
a logical value, if |
Details
The homoskedastic SVAR model is given by the reduced form equation:
Y = AX + E
where Y is an NxT matrix of dependent variables, X is a KxT matrix of explanatory variables,
E is an NxT matrix of reduced form error terms, and A is an NxK matrix of autoregressive slope coefficients and parameters on deterministic terms in X.
The structural equation is given by
BE = U
where U is an NxT matrix of structural form error terms, and
B is an NxN matrix of contemporaneous relationships.
The structural shocks, U, are temporally and contemporaneously independent and jointly normally distributed with zero mean and unit variances.
The various SVAR models estimated differ by the specification of structural shocks
variances. Their specification depends on the specify_bsvar* function used. The different models include:
homoskedastic model with unit variances
heteroskedastic model with stationary Markov switching in the variances
heteroskedastic model with Stochastic Volatility process for variances
non-normal model with a finite mixture of normal components and component-specific variances
heteroskedastic model with sparse Markov switching in the variances where the number of heteroskedastic components is estimated
non-normal model with a sparse mixture of normal components and component-specific variances where the number of heteroskedastic components is estimated
Value
An object of class PosteriorBSVAR, PosteriorBSVARMSH, PosteriorBSVARMIX, or PosteriorBSVARSV containing the Bayesian estimation output and containing two elements:
posterior a list with a collection of S draws from the posterior distribution generated via Gibbs sampler containing many arrays and vectors whose selection depends on the model specification.
last_draw an object of class BSVAR, BSVARMSH, BSVARMIX, or BSVARSV with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using estimate().
Author(s)
Tomasz Woźniak wozniak.tom@pm.me
References
Sampling from the generalised-normal full conditional posterior distribution of matrix B is implemented using the Gibbs sampler by:
Waggoner, D.F., and Zha, T., (2003) A Gibbs sampler for structural vector autoregressions. Journal of Economic Dynamics and Control, 28, 349–366, doi:10.1016/S0165-1889(02)00168-9.
Sampling from the multivariate normal full conditional posterior distribution of each of the A matrix row is implemented using the sampler by:
Chan, J.C.C., Koop, G, and Yu, X. (2021) Large Order-Invariant Bayesian VARs with Stochastic Volatility.
See Also
specify_bsvar, specify_bsvar_msh, specify_bsvar_mix, specify_bsvar_sv, normalise_posterior
Examples
# simple workflow
############################################################
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar$new(us_fiscal_lsuw, p = 4)
set.seed(123)
# run the burn-in
burn_in = estimate(specification, 50)
# estimate the model
posterior = estimate(burn_in, 100)
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar$new(p = 1) |>
estimate(S = 50) |>
estimate(S = 100) |>
compute_impulse_responses(horizon = 8) -> irf