DEPRECATED! USE THE FUNCTION fitGSMVAR INSTEAD!
gsmvar_to_sgsmvar constructs SGMVAR model based on a reduced
form GMVAR, StMVAR, or G-StMVAR model.
object of class 'gmvar'
should approximate standard errors be calculated?
The switch is made by simultaneously diagonalizing the two error term covariance matrices with a well known matrix decomposition (Muirhead, 1982, Theorem A9.9) and then normalizing the diagonal of the matrix W positive (which implies positive diagonal of the B-matrix). Models with more that two regimes are not supported because the matrix decomposition does not generally exists for more than two covariance matrices. If the model has only one regime (= regular SVAR model), a symmetric and pos. def. square root matrix of the error term covariance matrix is used.
The columns of W as well as the lambda parameters can be re-ordered (without changing the implied
reduced form model) afterwards with the function
reorder_W_columns. Also all signs in any column
of W can be swapped (without changing the implied reduced form model) afterwards with the function
swap_W_signs. These two functions work with models containing any number of regimes.
Returns an object of class
'gsmvar' defining a structural GMVAR, StMVAR, or G-StMVAR model based on a
two-regime reduced form GMVAR, StMVAR, or G-StMVAR model, with the main diagonal of the B-matrix normalized to be
Muirhead R.J. 1982. Aspects of Multivariate Statistical Theory, Wiley.
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
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 Unpublished working paper, available as arXiv:2007.04713.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 Unpublished working paper, available as arXiv:2109.13648.
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