stmar_to_gstmar {uGMAR}

Estimate a G-StMAR model based on a StMAR model with large degrees of freedom parameters

Description

stmar_to_gstmar estimates a G-StMAR model based on a StMAR model with large degree of freedom parameters.

Usage

stmar_to_gstmar(
  gsmar,
  maxdf = 100,
  estimate,
  calc_std_errors,
  maxit = 100,
  custom_h = NULL
)

Arguments

gsmar

a class 'gsmar' object, typically generated by fitGSMAR or GSMAR.

maxdf

regimes with degrees of freedom parameter value larger than this will be turned into GMAR type.

estimate

set TRUE if the new model should be estimated with a variable metric algorithm using the StMAR model parameter value as the initial value. By default TRUE iff the model contains data.

calc_std_errors

set TRUE if the approximate standard errors should be calculated. By default TRUE iff the model contains data.

maxit

the maximum number of iterations for the variable metric algorithm. Ignored if estimate==FALSE.

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.

Details

If a StMAR model contains large estimates for the degrees of freedom parameters, one should consider switching to the corresponding G-StMAR model that lets the corresponding regimes to be GMAR type. stmar_to_gstmar does this switch conveniently.

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).

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

Examples

 # These are long running example that take approximately 15 seconds to run.
 fit42t <- fitGSMAR(data=M10Y1Y, p=4, M=2, model="StMAR", ncalls=1, seeds=6)
 fit42t # Overly large degrees of freedom estimate!

 # Switch to the appropriate G-StMAR model:
 fit42gs <- stmar_to_gstmar(fit42t)
 fit42gs