fitGMVAR
alt_gmvar
constructs a GMVAR model based on results from an arbitrary estimation round of fitGMVAR
.
alt_gmvar(
gmvar,
which_round = 1,
which_largest,
calc_cond_moments = TRUE,
calc_std_errors = TRUE
)
an object of class 'gmvar'
created with fitGMVAR
or GMVAR
.
based on which estimation round should the model be constructed? An integer value in 1,...,ncalls
.
based on estimation round with which largest log-likelihood should the model be constructed?
An integer value in 1,...,ncalls
. For example, which_largest=2
would take the second largest log-likelihood
and construct the model based on the corresponding estimates. If used, then which_round
is ignored.
should conditional means and covariance matrices should be calculated?
Default is TRUE
if the model contains data and FALSE
otherwise.
should approximate standard errors be calculated?
Returns an object of class 'gmvar'
defining the specified reduced form or structural GMVAR model.
Can be used to work with other functions provided in gmvarkit
.
Remark that the first autocovariance/correlation matrix in $uncond_moments
is for the lag zero,
the second one for the lag one, etc.
It's sometimes useful to examine other estimates than the one with the highest log-likelihood. This function
is wrapper around GMVAR
that picks the correct estimates from an object returned by fitGMVAR
.
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Kalliovirta L. and Saikkonen P. 2010. Reliable Residuals for Multivariate Nonlinear Time Series Models. Unpublished Revision of HECER Discussion Paper No. 247.
Virolainen S. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.
# NOT RUN {
# GMVAR(1,2) model
fit12 <- fitGMVAR(gdpdef, p=1, M=2, ncalls=2, seeds=4:5)
fit12
fit12_2 <- alt_gmvar(fit12, which_largest=2)
fit12_2
# }
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