View source: R/parameterReforms.R
change_regime | R Documentation |
\upsilon_{m}
= (\phi_{m,0},
\phi_{m}
,\sigma_{m})
of the given parameter vectorchange_regime
changes the regime parameters (excluding mixing weights parameter)
of the pointed regime to the new given parameters.
change_regime(
p,
M,
d,
params,
model = c("GMVAR", "StMVAR", "G-StMVAR"),
m,
regime_pars,
structural_pars = NULL
)
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
d |
number of time series in the system, i.e. the dimension. |
params |
a real valued vector specifying the parameter values.
Above, In the GMVAR model, The notation is similar to the cited literature. |
model |
is "GMVAR", "StMVAR", or "G-StMVAR" model considered? In the G-StMVAR model, the first |
m |
which component? |
regime_pars |
In the case of a GMVAR type regime, |
structural_pars |
If
See Virolainen (forthcoming) for the conditions required to identify the shocks and for the B-matrix as well (it is |
Does not currently support models with AR, mean, alpha, or lambda parameter constraints.
Returns parameter vector with m
:th regime changed to regime_pars
.
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 Unpublished working paper, available as arXiv:2109.13648.
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