uGMAR (version 3.0.1)

swap_parametrization: Swap the parametrization of object of class 'gsmar' defining a gsmar model

Description

swap_parametrization swaps the parametrization of object of class 'gsmar' to "mean" if the currect parametrization is "intercept", and vice versa.

Usage

swap_parametrization(gsmar, calc_std_errors = TRUE)

Arguments

gsmar

object of class 'gsmar', generated by function fitGSMAR() or GSMAR().

calc_std_errors

should approximate standard errors be calculated?

Details

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.

References

• 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 G-StMAR model, but it's a straightforward generalization with theoretical properties similar to GMAR and StMAR models.

See Also

fitGSMAR, GSMAR, iterate_more, get_gradient, get_regime_means

Examples

# NOT RUN {
# GMAR model with intercept parametrization
params13 <- c(1.4, 0.88, 0.26, 2.46, 0.82, 0.74, 5.0, 0.68, 5.2, 0.72, 0.2)
gmar13 <- GSMAR(data=VIX, p=1, M=3, params=params13, model="GMAR")
gmar13

# Swap to mean parametrization
gmar13 <- swap_parametrization(gmar13)
gmar13
# }