uGMAR (version 3.2.6)

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
)

Arguments

gsmar

object of class 'gsmar' created with the function 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 estination 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 used, 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.

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

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.

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 {
 # These are long running examples and use parallel computing
 fit43t <- fitGSMAR(T10Y1Y, 4, 3, model="StMAR", ncalls=2, seeds=1:2)
 fit43t
 fit43t2 <- alt_gsmar(fit43t, which_largest=2)
 fit43t2
# }