Description Usage Arguments Value Warning References
View source: R/uncondMoments.R
get_regime_means
calculates regime means μ_{m} = (I  ∑ A)^(1))
from the given parameter vector.
1 2 3 4 5 6 7 8 9 10 11 
p 
a positive integer specifying the autoregressive order of the model. 
M 

d 
the number of time series in the system. 
params 
a real valued vector specifying the parameter values.
Above, φ_{m,0} is the intercept parameter, A_{m,i} denotes the ith coefficient matrix of the mth
mixture component, Ω_{m} denotes the error term covariance matrix of the m:th mixture component, and
α_{m} is the mixing weight parameter. The W and λ_{mi} are structural parameters replacing the
error term covariance matrices (see Virolainen, 2020). If M=1, α_{m} and λ_{mi} are dropped.
If In the GMVAR model, M1=M and ν is dropped from the parameter vector. In the StMVAR model, M1=0.
In the GStMVAR model, the first The notation is similar to the cited literature. 
model 
is "GMVAR", "StMVAR", or "GStMVAR" model considered? In the GStMVAR model, the first 
parametrization 

constraints 
a size (Mpd^2 x q) constraint matrix C specifying general linear constraints
to the autoregressive parameters. We consider constraints of form
(φ_{1},...,φ_{M}) = C ψ,
where φ_{m} = (vec(A_{m,1}),...,vec(A_{m,p}) (pd^2 x 1), m=1,...,M,
contains the coefficient matrices and ψ (q x 1) contains the related parameters.
For example, to restrict the ARparameters to be the same for all regimes, set C=
[ 
same_means 
Restrict the mean parameters of some regimes to be the same? Provide a list of numeric vectors
such that each numeric vector contains the regimes that should share the common mean parameters. For instance, if

structural_pars 
If
See Virolainen (2020) for the conditions required to identify the shocks and for the Bmatrix as well (it is W times a timevarying diagonal matrix with positive diagonal entries). 
Returns a (dxM) matrix containing regime mean μ_{m} in the m:th column, m=1,..,M.
No argument checks!
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485498.
Virolainen S. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.
Virolainen S. 2021. Gaussian and Student's t mixture vector autoregressive model. Unpublished working paper, available as arXiv:2109.13648.
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