uGMAR (version 3.5.2)

alt_gsmar: Construct a GSMAR model based on results from an arbitrary estimation round of fitGSMAR

Description

alt_gsmar constructs a GSMAR model based on results from an arbitrary estimation round of fitGSMAR.

Usage

alt_gsmar(
  gsmar,
  which_round = 1,
  which_largest,
  calc_qresiduals = TRUE,
  calc_cond_moments = TRUE,
  calc_std_errors = TRUE,
  custom_h = NULL
)

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).

Arguments

gsmar

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

which_round

based on which estimation round should the model be constructed? An integer value in 1,...,ncalls.

which_largest

based on estimation round with which largest log-likelihood should the model be constructed? An integer value in 1,...,ncalls. For example, which_largest=2 would take the second largest log-likelihood and construct the model based on the corresponding estimates. If specified, then which_round is ignored.

calc_qresiduals

should quantile residuals be calculated? Default is TRUE iff the model contains data.

calc_cond_moments

should conditional means and variances be calculated? Default is TRUE iff the model contains data.

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.

Details

It's sometimes useful to examine other estimates than the one with the highest log-likelihood value. This function is just a simple wrapper to GSMAR that picks the correct estimates from an object returned by fitGSMAR.

In addition to the S3 methods listed under the topic "Methods (by generic)", the predict and simulate methods are also available for the class 'gsmar' objects (see ?predict.gsmar and ?simulate.gsmar).

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

# \donttest{
 # These are long running examples that take approximately ...
 fit42t <- fitGSMAR(data=M10Y1Y, p=4, M=2, model="StMAR", ncalls=2,
                    seeds=c(1, 6))
 fit42t # Bad estimate in the boundary of the stationarity region!

 # So we build a model based on the next-best local maximum point:
 fit42t_alt <- alt_gsmar(fit42t, which_largest=2)
 fit42t_alt # Overly large degrees of freedom paramter estimate

 # Switch to the appropriate G-StMAR model:
 fit42gs <- stmar_to_gstmar(fit42t_alt)
 fit42gs
# }