posterior.fit {MSBVAR} | R Documentation |
Computes the marginal log likelihood
other posterior fit measures for VAR, BVAR, and BSVAR models fit with
szbsvar
and szbvar
.
posterior.fit(varobj, A0.posterior.obj=NULL) posterior.fit.BVAR(varobj) posterior.fit.BSVAR(varobj, A0.posterior.obj)
varobj |
Bayesian BVAR or BSVAR object from fitting a model with
szbsvar or szbvar |
A0.posterior.obj |
MCMC Gibbs sample object for the B-SVAR model A(0)
from gibbs.A0.BSVAR |
Estimates the marginal log likelihood, log prior, log posterior for the A_0 and A_1,...,A_p parameters of the model, and the log data density for the sample (after integrating out the model parameters). The approach used is that of Chib (1995)
BVAR:
A list of the class "posterior.fit.VAR" that includes the following
elements:
data.marg.llf |
Log marginal density, the probability of the data after integrating out the parameters in the model. |
data.marg.post |
Predictive marginal posterior density |
Coefficient log likelihood |
|
log.prior |
Log prior probability |
log.llf |
T x 1 list of the log probabilities for each observation conditional on the parameters. |
log.posterior.Aplus |
Log marginal probability of A(1),...,A(p) conditional on the data and A(0) |
log.marginal.data.density |
Log data density or marginal log likelihood, the probability of the data after integrating out the parameters in the model. |
log.marginal.A0k |
m x 1 list of the log probabilities of each column (corresponding to the equations) of A(0) conditional on the other columns. |
The log Bayes factor for two model can be computed using the log.marginal.data.density:
log BF = log.marginal.data.density.1 - log.marginal.data.density.2
Note that at present, the scale factors for the BVAR and B-SVAR models
are different (one used the concentrated likelihood, the other does
NOT). Thus, one cannot compare fit measures across the two
functions. To compare a recursive B-SVAR to a non-recursive B-SVAR
model, one should estimate the recursive model with szbsvar
using the appropriate ident
matrix and then call
posterior.fit
on the two B-SVAR models!
Patrick T. Brandt
Chib, Siddartha. 1995. "Marginal Likelihood from the Gibbs Output." Journal of the American Statistical Association. 90(432): 1313–1321.
Waggoner, Daniel F. and Tao A. Zha. 2003. "A Gibbs sampler for structural vector autoregressions" Journal of Economic Dynamics & Control. 28:349–366.
szbvar
,
szbsvar
,
gibbs.A0.BSVAR
,
mc.irf
,
print.posterior.fit
## Not run: varobj <- szbsvar(Y, p, z = NULL, lambda0, lambda1, lambda3, lambda4, lambda5, mu5, mu6, ident, qm = 4) A0.posterior <- gibbs.A0.BSVAR(varobj, N1, N2) fit <- posterior.fit(varobj, A0.posterior) print(fit) ## End(Not run)