Mstep.hh.MSAR.with.constraints

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

number of sites. For instance, if one considers wind at k locations, K=k. Or more generally number of independent groups of components.

dimension in each sites. For instance, if one considers only wind intensity than d.y = 1; but, if one considers cartesian components of wind, then d.y =2.

M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with constraints on VAR models, called in fit.MSAR. Maximum likelihood is used. Matrices A and sigma are diagonal by blocks.

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.MSAR.with.constraints function

M step of the EM algorithm for fitting  homogeneous multivariate Markov switching auto-regressive models with constraints on VAR models. — Mstep.hh.MSAR.with.constraints

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

Description

Usage

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Value

See Also