Mstep.hn.MSAR: M step of the EM algorithm for fitting Markov switching...

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

The M step contains two parts. One for the estimation of the parameters of the hidden Markov chain and the other for the parameters of the auto-regressive models. A numerical algortihm is used for the emission parameters.

Usage

Mstep.hn.MSAR(data, theta, FB, covar = NULL, verbose = 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
covar	emissions covariates (the covariables act on the intercepts)
verbose	if verbose is TRUE some iterations of the numerical optimisation are print on the console.

Details

The default numerical optimization method is ucminf (see ucminf).

Value

List containing

..$A0	intercepts
..$A	AR coefficients
..$sigma	variance of innovation
..$prior	prior probabilities
..$transmat	transition matrix
..$par_emis	emission parameters

Author(s)

Valerie Monbet, [e-mail]

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, init.theta.MSAR, Mstep.hh.MSAR

NHMSAR documentation built on Feb. 9, 2022, 9:06 a.m.