Mstep.nn.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

covar.trans

emissions covariates (the covariates act on the intercepts)

covar.emis

permits to choice the optimization algorithm. default is "ucminf", other possible choices are "BFGS" or "L-BFGS-B

method

M step of the EM algorithm for fitting Markov switching auto-regressive models with non homogeneous emissions and non homogeneous transitions.

 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.nn.MSAR: M step of the EM algorithm.

Description

Usage

Arguments

Value

References

See Also