uGMAR (version 3.1.0)

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

Description

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

Usage

iterate_more(gsmar, maxit = 100)

Arguments

gsmar

object of class 'gsmar' created with the function fitGSMAR or GSMAR.

maxit

maximum number of iterations in the variable metric algorithm.

Value

Returns an object of class 'gsmar' defining the estimated model. Can be used to work with other functions provided in uGMAR.

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. It's just a simple wrapper around function optim from the package stats and GSMAR from the package uGMAR.

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].

• There are currently no published references for the G-StMAR model, but it's a straightforward generalization with theoretical properties similar to the GMAR and StMAR models.

See Also

fitGSMAR, GSMAR, stmar_to_gstmar, optim

Examples

# NOT RUN {
# Estimate GMAR model with only 50 generations of genetic algorithm and
# only 1 iteration in variable metric algorithm
fit12 <- fitGSMAR(logVIX, 1, 2, maxit=1, ngen=50, ncalls=3)
fit12

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