Mstep.hh.lasso.MSAR

array of univariate or multivariate series with dimension T x N.samples x 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

M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with penalization of parameters of the VAR(1) models, called in fit.MSAR. Penalized maximum likelihood is used. Penalization may be add to the autoregressive matrices of order 1 and to the precision matrices (inverse of variance of innovation).

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.

Valerie Monbet

NHMSAR

Non-Homogeneous Markov Switching Autoregressive Models

Mstep.hh.lasso.MSAR function

M step of the EM algorithm for fitting  homogeneous multivariate Markov switching auto-regressive models with penalization of parameters of the VAR(1) models. — Mstep.hh.lasso.MSAR

M step of the EM algorithm for fitting  homogeneous multivariate Markov switching auto-regressive models with penalization of parameters of the VAR(1) models.

Mstep.hh.lasso.MSAR: M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with penalization of parameters of the VAR(1) models.

Description

Usage

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Value

Details

References

See Also