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.
The computations are done using compiled C++ code as of version 0.3.0. See the package source code for details about the implementation.
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. |
You must have called / loaded an szbsvar
object to
use this Gibbs sampler.
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
# SZ, B-SVAR model for the Levant data data(BCFdata) m <- ncol(Y) ident <- diag(m) ident[1,] <- 1 ident[2,1] <- 1 # estimate the model's posterior moments set.seed(123) model <- szbsvar(Y, p=2, z=z2, lambda0=0.8, lambda1=0.1, lambda3=1, lambda4=0.1, lambda5=0.05, mu5=0, mu6=5, ident, qm=12) # Set length of burn-in and size of posterior. These are only an # example. Production runs should set these much higher. N1 <- 1000 N2 <- 1000 A0.posterior.obj <- gibbs.A0(model, N1, N2, thin=1) # Use coda to look at the posterior. A0.free <- A02mcmc(A0.posterior.obj) plot(A0.free)