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

Description

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.

Usage

1

Arguments

data

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.

theta

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

FB

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

K

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

d.y

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.

Value

A0

intercepts

A

AR coefficients

sigma

variance of innovation

prior

prior probabilities

transmat

transition matrix

Author(s)

Valerie Monbet, valerie.monbet@univ-rennes1.fr

See Also

Mstep.hh.MSAR, fit.MSAR, Mstep.hh.SCAD.MSAR

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