uGMAR (version 3.2.4)

swap_parametrization: Swap the parametrization of object of class 'gsmar' defining a GMAR, StMAR, or G-StMAR model

Description

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

Usage

swap_parametrization(gsmar, calc_std_errors = TRUE, custom_h = NULL)

Arguments

gsmar

object of class 'gsmar' created with the function fitGSMAR or GSMAR.

calc_std_errors

should approximate standard errors be calculated?

custom_h

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.

Value

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).

Details

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.

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].

• Virolainen S. 2020. A mixture autoregressive model based on Gaussian and Student's t-distribution. arXiv:2003.05221 [econ.EM].

See Also

fitGSMAR, GSMAR, iterate_more, get_gradient, get_regime_means, swap_parametrization, stmar_to_gstmar

Examples

# 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)
# }