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)
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
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.