initialize.opt argument of the msbvar function.
Users can inputs values outside of the defaults for the Q
transition matrix and other arguments with this function. This
function also serves as a model for alternative, user-defined initial
values for the Gibbs sampler.initialize.msbvar(y, p, z = NULL, lambda0, lambda1, lambda3, lambda4,
lambda5, mu5, mu6, nu, qm, prior, h, Q = NULL)initialize.opt argument in msbvar)szbvar for detailsmsbvar. This is an $m \times (mp + 1 + m) \times
h$ array of the initial coefficients. For the
ith element of the array, the
$m$ rows refer to the equations, the first column elements are
the intercepts, the next $2:(mp + 1)$ columns are the AR(p)
coefficients, and the final $m \times m$ elements are the
error covariance for the regime, for that array element.Qmsbvar. This is an attempt to (1) allow for a robust,
smart guess for starting the block-optimization algorithm and (2)
allow for user inputs to initialize.opt.The function does three things:
(1) Estimates an initial szbvar model as a baseline,
non-regime switching model.
(2) Estimates a set of h VAR regressions based on a
kmeans clustering of the time series with $h$
clusters or centers. The VAR models fit to each of the $h$
subsets of data are used to initialize the msbvar
function.
(3) Sets an initial value for Q in the block optimization
algorithm for the mode of the MLE / posterior for the MSBVAR model.
If Q=NULL, for an $h \times h$ transition matrix
Q, this initial value is set based on the results from the
kmeans clustering of the data. If the user inputs a
value of Q, this is used and error checked to make sure it has
the correct format (i.e., rows sum to 1, etc.)
msbvar