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 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).
swap_parametrization is a convenient tool if you have estimated the model in
"intercept"-parametrization but wish to work with "mean"-parametrization in the future,
or vice versa. For example, approximate standard errors are readily available for
parametrized parameters only.
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 {
# GMAR model with intercept parametrization
params12 <- c(0.183, 0.927, 0.002, 0.857, 0.682, 0.019, 0.883)
gmar12 <- GSMAR(data=logVIX, p=1, M=2, params=params12, model="GMAR",
calc_std_errors=TRUE)
summary(gmar12)
# Swap to mean parametrization
gmar12 <- swap_parametrization(gmar12)
summary(gmar12)
# }
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