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
a class 'gsmar' object, typically generated by
the maximum number of iterations for the variable metric algorithm.
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.
should approximate standard errors be calculated?
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
iterate_more is essentially a wrapper for the functions
optim from the package
GSMAR from the package
Returns an object of class
'gsmar' defining the estimated model.
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. 2021. A mixture autoregressive model based on Student's t-distribution. Communications in Statistics - Theory and Methods, doi: 10.1080/03610926.2021.1916531
Virolainen S. 2021. A mixture autoregressive model based on Gaussian and Student's t-distributions. Studies in Nonlinear Dynamics & Econometrics, doi: 10.1515/snde-2020-0060
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