gibbs.A0 {MSBVAR} | R Documentation |
Samples from the structural contemporaneous parameter matrix A(0) of a Bayesian Structural Vector Autoregression (B-SVAR) model.
gibbs.A0(varobj, N1, N2, thin=1, normalization="DistanceMLA")
varobj |
A structural BVAR object created by
szbsvar |
N1 |
Number of burn-in iterations for the Gibbs sampler (should probably be greater than or equal to 1000) |
N2 |
Number of iterations in the posterior sample. |
thin |
Thinning parameter for the Gibbs sampler. |
normalization |
Normalization rule as defined in
normalize.svar . Default is "DistanceMLA" as
recommended in Waggoner and Zha (2003b). |
Samples the posterior pdf of an A(0) matrix for a Bayesian
structural VAR using the algorithm described in Waggoner and Zha
(2003a). This function is meant to be called after
szbsvar
, so one should consult that function
for further information. The function draws N2 * thin
draws
from the sampler and returns the N2
draws that are the
thin
'th elements of the Gibbs sampler sequence.
A list of five elements:
A0.posterior |
A list of three elements containing the results
of the N2 A(0) draws. The list contains a vector
storing all of the draws, the location of the drawn elements in and
the dimension of A(0). A0.posterior$A0 is a vector
of length equal to the number of parameters in A(0) times N2.
A0.posterior$struct is a vector of length equal to the number of
free parameters in A(0) that gives the index positions
of the elements in A(0). A0.posterior$m is
m, an integer, the number of equations in the system.
|
W.posterior |
A list of three elements that describes the
vectorized W
matrices that characterize the covariance of the restricted
parameter space of each column of A(0).
W.posterior$W is a vector of the elements of all the sampled
W matrices. W.posterior$W.index is a cumulative index
of the elements of
W that defines how the W matrices for each iteration
of the sampler are stored in the vector.
W.posterior$m is m, an integer, the number of equations
in the system. |
ident |
ident matrix from the varobj of binary
elements that defined the free and restricted parameters, as
specified in szbsvar |
thin |
thin value that was input into the function for
thinning the Gibbs sampler. |
N2 |
N2 , size of the posterior sample. |
Patrick T. Brandt
Waggoner, Daniel F. and Tao A. Zha. 2003a. "A Gibbs sampler for structural vector autoregressions" Journal of Economic Dynamics & Control. 28:349–366.
Waggoner, Daniel F. and Tao A. Zha, 2003b. "Likelihood Preserving Normalization in Multiple Equation Models" Journal of Econometrics, 114: 329–347
szbsvar
for estimation of the
posterior moments of the B-SVAR model,
normalize.svar
for a discussion of and references on
A(0) normalization.
posterior.fit
for computing the
marginal log likelihood for the model after sampling the posterior
## Not run: varobj <- szbsvar(Y, p, z = NULL, lambda0, lambda1, lambda3, lambda4, lambda5, mu5, mu6, ident, qm = 4) N1 <- 100 N2 <- 1000 A0posterior.obj <- gibbs.A0(varobj, N1, N2, thin=10) # Use coda to look at the posterior. library(coda) A0.free <- A02mcmc(A0.posterior.obj) plot(A0.free) ## End(Not run)