fitGSMARalt_gsmar constructs a GSMAR model based on results from an arbitrary estimation round of fitGSMAR.
alt_gsmar(
gsmar,
which_round = 1,
which_largest,
calc_qresiduals = TRUE,
calc_cond_moments = TRUE,
calc_std_errors = TRUE,
custom_h = NULL
)object of class 'gsmar' created with the function fitGSMAR or GSMAR.
based on which estimation round should the model be constructed? An integer value in 1,...,ncalls.
based on estination 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 quantile residuals be calculated? Default is TRUE iff the model contains data.
should conditional means and variances be calculated? Default is TRUE iff the model contains data.
should approximate standard errors be calculated?
A numeric vector with same the length as the parameter vector: i:th element of custom_h is the difference
used in central difference approximation for partial differentials of the log-likelihood function for the i:th parameter.
If NULL (default), then the difference used for differentiating overly large degrees of freedom parameters
is adjusted to avoid numerical problems, and the difference is 6e-6 for the other parameters.
Returns an object of class 'gsmar' defining the specified GMAR, StMAR, or G-StMAR model. If data is supplied,
the returned object contains (by default) empirical mixing weights, some conditional and unconditional moments, and quantile
residuals. Note that the first p observations are taken as the initial values so the mixing weights, conditional moments, and
quantile residuals start from the p+1:th observation (interpreted as t=1).
It's sometimes useful to examine other estimates than the one with the highest log-likelihood value. This function
is just a simple wrapper to GSMAR that picks the correct estimates from an object returned by fitGSMAR.
Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. Journal of Time Series Analysis, 36, 247-266.
Meitz M., Preve D., Saikkonen P. 2018. A mixture autoregressive model based on Student's t-distribution. arXiv:1805.04010 [econ.EM].
Virolainen S. 2020. A mixture autoregressive model based on Gaussian and Student's t-distribution. arXiv:2003.05221 [econ.EM].
fitGSMAR, GSMAR, iterate_more, get_gradient,
get_regime_means, swap_parametrization, stmar_to_gstmar
# NOT RUN {
# These are long running examples and use parallel computing
fit12t <- fitGSMAR(logVIX, 1, 2, model="StMAR", ncalls=2, seeds=1:2)
fit12t
fit12t2 <- alt_gsmar(fit12t, which_round=2)
fit12t2
# }
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