Usage
MCMCpanic04(x, nfac, k1, criteria = NULL,burn = 1000,
mcmc = 10000, thin = 10, verbose = 0, seed = NA,
lambda.start = NA, psi.start = NA, l0 = 0, L0 = 0,
a0 = 0.001, b0 = 0.001, std.var = TRUE,...)
Arguments
x
An object of class xts with each column being a time series
nfac
An integer specifying the maximum number of factors allowed
while estimating the factor model.
k1
an integer that is the maximum lag allowed in the ADF test.
criteria
a character vector of length one with a value of either
IC1, IC2, IC3, AIC1, BIC1, AIC3, BIC3, or eigen. Choosing eigen makes
the number of factors equal to the number of columns whose sum of
eigenvalues is less than or equal to .5.
burn
Integer of the number of burn in iterators for the sampler
mcmc
Integer of the number of iterations in the sampler
thin
Integer of the thinning interval used in the simulation. mcmc must be divisible by this value.
verbose
A positive integer which determines whether or not the progress of the
sampler is printed to the screen. If verbose is greater than 0 the iteration
number and the factor loadings and uniquenesses are printed to the screen
every verboseth iteration.
seed
The seed for the random number generator.
lambda.start
Starting values for the factor loading matrix Lambda.
psi.start
Starting values for the uniqueness
l0
The means of the independent Normal prior on the factor loadings
L0
A scalar or a matrix with the same dimensions as lambda. The precision (inverse variances)
of the independent Normal prior on the factor loadings.
a0
scalar or a k-vector. Controls the shape of the inverse Gamma prior on the uniqueness.
b0
Controls the scale of the inverse Gamma prior on the uniqueness.
std.var
if TRUE the variables are rescaled to have zero mean and unit variance.
Otherwise, the variables are rescaled to have zero mean, but retain their observed variances
...
extra parameters to pass to MCMCfactanal