Mstep.hh.reduct.MSAR

array of univariate or multivariate series with dimension T*N.samples*d.
T: number of time steps of each sample, N.samples: number of realisations of the same stationary process, d: dimension.

data

model's parameter; object of class MSAR. See also init.theta.MSAR.

theta

Forward-Backward results, obtained by calling Estep.MSAR function

if TRUE the innovation covariance matrices are diagonal.

sigma.diag

M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive model swith constraints on the matrices, called in fit.MSAR. The matrices are constrained to have the same pattern ()zeros and non zeros coefficients) as the initial matrices.

 EM algorithm

 M step 

Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.

Mstep.hh.reduct.MSAR: M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models with constraints on the matrices.

Description

Usage

Arguments

Value

References

See Also