iterate_more() R function from [uGMAR]

Maximum likelihood estimation of GMAR, StMAR, or G-StMAR model with preliminary estimates

iterate_more uses a variable metric algorithm to finalize maximum likelihood estimation of a GMAR, StMAR or G-StMAR model (object of class 'gsmar') which already has preliminary estimates.


iterate_more(gsmar, maxit = 100, custom_h = NULL, calc_std_errors = TRUE)

Arguments

gsmar: a class 'gsmar' object, typically generated by fitGSMAR or GSMAR.
maxit: the maximum number of iterations for the variable metric algorithm.
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.
calc_std_errors: should approximate standard errors be calculated?

Returns

Returns an object of class 'gsmar' defining the estimated model.

Details

The main purpose of iterate_more is to provide a simple and convenient tool to finalize the estimation when the maximum number of iterations is reached when estimating a model with the main estimation function fitGSMAR. iterate_more is essentially a wrapper for the functions optim from the package stats and GSMAR from the package uGMAR.

Examples


# Estimate GMAR model with on only 1 iteration in variable metric algorithm
fit12 <- fitGSMAR(simudata, p=1, M=2, maxit=1, ncalls=1, seeds=1)
fit12

# Iterate more since iteration limit was reached
fit12 <- iterate_more(fit12)
fit12

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.

iterate_more function