Description Usage Arguments Details Value Note Author(s) References See Also Examples
Identifies and plots regime-specific coefficients from the random permutation sampler for regime identification
1 2 3 | plotregimeid(x,
type = c("all", "intercepts", "AR1", "Sigma", "Q"),
ask = TRUE, ...)
|
x |
Gibbs sampler output of class |
type |
Items to be clustered and plots to be produced to identify
the posterior regimes / modes of the Gibbs sampler based on the
randomly permuted draws. The |
ask |
logical, |
... |
Optional graphical and lattice parameters to be fed to the plots. There is no assurance that these will work. E-mail if you have inputs on this that do not work, but that you think should. |
The posterior of a Markov-switching (MS) model estimated by an
unrestricted Gibbs sampler has h! identical posterior modes.
The modes are identical in the sense that they are merely relabelings
of the regime labels. Since the analyst may not apriori know what
defines or separates the regimes in the parameter space, this function
allows one to explore the randomly permuted labelings that are
generated by the gibbs.msbvar
function.
This function takes the permuted output of gibbs.msbvar
and shows
colored pairs, scatter, densityplots, and traceplots for the posterior
parameters. The
coloring follow standard R color pallates. The determination of how
the regimes are identified is based on a kmeans
clustering of either the the parameters "intercepts", "AR1", "Sigma"
(variances), or "Q" transition probabilities. This is the method
suggested by Fruhwirth-Schanatter (2001, 2006). The utility here is
that this function handles subsetting the data, setting up the
clustering and plotting and labeling the results for the user.
Regime identification and labeling is necessary so that one can sample
from a single mode of the posterior to get sensible regime
classification plots from say plot.SS
or regime
probabilities from say mean.SS
.
None. A series of plots are produced in the current graphics device.
This is the first version of this function. Future versions may use a slightly different syntax and only use one input argument.
Patrick T. Brandt
Fruhwirth-Schnatter, Sylvia. 2001. "Markov Chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models". Journal of the American Statistical Association. 96(153):194–209.
Fruhwirth-Schnatter, Sylvia. 2006. Finite Mixture and Markov Switching Models. Springer Series in Statistics New York: Springer.
msbvar
, plot.SS
,
mean.SS
, gibbs.msbvar
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ## Not run:
# This example can be pasted into a script or copied into R to run. It
# takes a few minutes, but illustrates how the code can be used
data(IsraelPalestineConflict)
# Find the mode of an msbvar model
# Initial guess is based on random draw, so set seed.
set.seed(123)
xm <- msbvar(IsraelPalestineConflict, p=1, h=2,
lambda0=0.8, lambda1=0.15,
lambda3=2, lambda4=1, lambda5=0, mu5=0,
mu6=0, qm=12,
alpha.prior=matrix(c(100,40,30,50), 2, 2))
# Plot out the initial mode
plot(ts(xm$fp))
print(xm$Q)
# Now sample the posterior
N1 <- 100
N2 <- 500
# First, so this with random permutation sampling
x1 <- gibbs.msbvar(xm, N1=N1, N2=N2, permute=TRUE)
# Identify the regimes using clustering in plotregimeid()
plotregimeid(x1, type="all")
# Now re-estimate based on desired regime identification seen in the
# plots. Here we are using the variance of the first equation, so
# Sigma.idx=1.
x2 <- gibbs.msbvar(xm, N1=N1, N2=N2, permute=FALSE, Sigma.idx=1)
# Plot the variances. Note the strict hyperplane between the variances
# for the first equation versus the others.
plotregimeid(xm, x2, type="Sigma")
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.