| estimate_bsvar_mix {bsvars} | R Documentation |
Estimates the SVAR with non-normal residuals following a finite M mixture of normal distributions proposed by Woźniak & Droumaguet (2022).
Implements 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 thanks to a 3-level hierarchical prior distribution. The finite mixture of normals
model is estimated using the prior distributions and algorithms proposed by Woźniak & Droumaguet (2022).
See section Details for the model equations.
estimate_bsvar_mix(S, specification, thin = 10, show_progress = TRUE)
S |
a positive integer, the number of posterior draws to be generated |
specification |
an object of class BSVAR-MIX generated using the |
thin |
a positive integer, specifying the frequency of MCMC output thinning |
show_progress |
a logical value, if |
The heteroskedastic 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.
Finally, the structural shocks, U, are temporally and contemporaneously independent and finite-mixture of normals distributed with zero mean.
The conditional variance of the nth shock at time t is given by:
Var_{t-1}[u_{n.t}] = s^2_{n.s_t}
where s_t is a the regime indicator of
the regime-specific conditional variances of structural shocks s^2_{n.s_t}.
In this model, the variances of each of the structural shocks sum to M.
The regime indicator s_t is either such that:
the regime probabilities are non-zero which requires all regimes to have a positive number occurrences over the sample period, or
sparse with potentially many regimes with zero occurrences over the sample period and in which the number of regimes is estimated.
These model selection also with this respect is made using function specify_bsvar_mix.
An object of class PosteriorBSVAR-MIX 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:
an NxKxS array with the posterior draws for matrix A
an NxNxS array with the posterior draws for matrix B
a 5xS matrix with the posterior draws for the hyper-parameters of the hierarchical prior distribution
an NxMxS array with the posterior draws for the structural shocks conditional variances
an MxMxS array with the posterior draws for the transition matrix.
an MxTxS array with the posterior draws for the regime allocation matrix.
an MxS matrix with the posterior draws for the ergodic probabilities
last_draw an object of class BSVAR-MIX with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using bsvar_mix().
Tomasz Woźniak wozniak.tom@pm.me
The model, prior distributions, and estimation algorithms were proposed by
Woźniak, T., and Droumaguet, M., (2022) Bayesian Assessment of Identifying Restrictions for Heteroskedastic Structural VARs
Some more analysis on heteroskedastic SVAR models was proposed by:
Lütkepohl, H., and Woźniak, T., (2020) Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity. Journal of Economic Dynamics and Control 113, 103862, doi:10.1016/j.jedc.2020.103862.
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.
The estimation of the mixture of normals heteroskedasticity closely follows procedures described by:
Song, Y., and Woźniak, T., (2021) Markov Switching. Oxford Research Encyclopedia of Economics and Finance, Oxford University Press, doi:10.1093/acrefore/9780190625979.013.174.
and
Frühwirth-Schnatter, S., (2006) Finite Mixture and Markov Switching Models. Springer Series in Statistics. New York: Springer, doi:10.1007/978-0-387-35768-3.
The sparse model is inspired by:
Malsiner-Walli, G., Frühwirth-Schnatter, S., and Grün, B. (2016) Model-based clustering based on sparse finite Gaussian mixtures. Statistics and Computing, 26(1–2), 303–324, doi:10.1007/s11222-014-9500-2.
The forward-filtering backward-sampling is implemented following the proposal by:
Chib, S. (1996) Calculating posterior distributions and modal estimates in Markov mixture models. Journal of Econometrics, 75(1), 79–97, doi:10.1016/0304-4076(95)01770-4.
specify_bsvar_mix, specify_posterior_bsvar_mix, normalise_posterior
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_mix$new(us_fiscal_lsuw, p = 4, M = 2)
set.seed(123)
# run the burn-in
burn_in = estimate_bsvar_mix(50, specification)
# estimate the model
posterior = estimate_bsvar_mix(100, burn_in$get_last_draw())