NHMSAR (version 1.19)

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

Description

M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models, called in fit.MSAR.

Usage

Mstep.hh.MSAR(data, theta, FB,sigma.diag=FALSE,sigma.equal=FALSE)

Arguments

data

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.

theta

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

FB

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

sigma.diag

If sigma.diag==TRUE the estimated covariance of the innovation will be diagonal (default is FALSE) - available only for HH models

sigma.equal

If sigma.equal==TRUE the estimated covariance of the innovation will be the same in all regimes - available only for models with homogeneous emission probabilities (default is FALSE)

Value

A list containing

A0

intercepts

A

AR coefficients

sigma

variance of innovation

prior

prior probabilities

transmat

transition matrix

References

Ailliot P., Monbet V., (2012), Markov switching autoregressive models for wind time series. Environmental Modelling & Software, 30, pp 92-101.

See Also

fit.MSAR, Estep.MSAR, Mstep.classif