swap_parametrization function

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

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)

Arguments

• gsmar: a class 'gsmar' object, typically generated by 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.

Returns

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.

Examples

# G-StMAR model with intercept parametrization
params42gs <- c(0.04, 1.34, -0.59, 0.54, -0.36, 0.01, 0.06, 1.28, -0.36,
                0.2, -0.15, 0.04, 0.19, 9.75)
gstmar42 <- GSMAR(data=M10Y1Y, p=4, M=c(1, 1), params=params42gs,
                  model="G-StMAR")
summary(gstmar42)

# Swap to mean parametrization
gstmar42 <- swap_parametrization(gstmar42)
summary(gstmar42)

References

• Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. Journal of Time Series Analysis, 36 (2), 247-266.
• Meitz M., Preve D., Saikkonen P. 2023. A mixture autoregressive model based on Student's t-distribution. Communications in Statistics - Theory and Methods, 52 (2), 499-515.
• Virolainen S. 2022. A mixture autoregressive model based on Gaussian and Student's t-distributions. Studies in Nonlinear Dynamics & Econometrics, 26 (4) 559-580.

See Also

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