swap_parametrization swaps the parametrization of object of class 'gsmar'
to "mean" if the current parametrization is "intercept", and vice versa.
swap_parametrization(gsmar, calc_std_errors = TRUE, custom_h = NULL)object of class 'gsmar' created with the function fitGSMAR or GSMAR.
should approximate standard errors be calculated?
A numeric vector of with same length as the parameter vector of the estimated model: i:th element
of custom_h is the difference used in central difference approximation for 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, conditional means and variances and quantile residuals. Note that the first p observations are
taken as the initial values so mixing weights, conditional moments and qresiduals start from the p+1:th observation (interpreted as t=1).
swap_parametrization is convenient tool if you have estimated the model in
"intercept"-parametrization, but wish to work with "mean"-parametrization in the future, or vice versa.
In gsmarkit, for example the approximate standard errors are only available for
parametrized parameters.
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].
There are currently no published references for the G-StMAR model, but it's a straightforward generalization with theoretical properties similar to the GMAR and StMAR models.
fitGSMAR, GSMAR, iterate_more, get_gradient,
get_regime_means, swap_parametrization, stmar_to_gstmar
# NOT RUN {
# GMAR model with intercept parametrization
params12 <- c(0.18, 0.93, 0.01, 0.86, 0.68, 0.02, 0.88)
gmar12 <- GSMAR(data=logVIX, p=1, M=2, params=params12, model="GMAR")
gmar12
# Swap to mean parametrization
gmar12 <- swap_parametrization(gmar12)
gmar12
# }
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