form_boldA creates the "bold A" coefficient matrices related to
VAR processes.
form_boldA(p, M, d, all_A)Returns 3D array containing the \(((dp)x(dp))\) "bold A" matrices related to each component VAR-process.
The matrix \(A_{m}\) can be obtained by choosing [, , m].
a positive integer specifying the autoregressive order of the model.
a positive integer specifying the number of mixture components.
a size (2x1) integer vector specifying the number of GMVAR type components M1
in the first element and StMVAR type components M2 in the second element. The total number of mixture components
is M=M1+M2.
the number of time series in the system.
4D array containing all coefficient matrices \(A_{m,i}\), obtained from pick_allA.
No argument checks!
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Virolainen S. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.
Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.
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