gmvarkit (version 1.1.1)

iterate_more: Maximum likelihood estimation of GMVAR model with preliminary estimates

Description

iterate_more uses variable metric algorithm to finalize maximum likelihood estimation of GMVAR model (object of class 'gmvar') which already has preliminary estimates.

Usage

iterate_more(gmvar, maxit = 100, calc_std_errors = TRUE)

Arguments

gmvar

object of class 'gmvar' created with fitGMVAR or GMVAR.

maxit

the maximum number of iterations in the variable metric algorithm.

calc_std_errors

should approximate standard errors be calculated?

Value

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

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 GMVAR model with the main estimation function fitGMVAR. It's just a simple wrapper around function optim from the package stats and GMVAR from the package gmvarkit.

References

• Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.

• Kalliovirta L. and Saikkonen P. 2010. Reliable Residuals for Multivariate Nonlinear Time Series Models. Unpublished Revision of HECER Discussion Paper No. 247.

See Also

fitGMVAR, GMVAR, optim

Examples

# NOT RUN {
## These are long running examples that use parallel computing!

# These examples use the data 'eurusd' which comes with the
# package, but in a scaled form.
data <- cbind(10*eurusd[,1], 100*eurusd[,2])
colnames(data) <- colnames(eurusd)

# GMVAR(1,2) model, only 5 iterations of the variable metric
# algorithm
fit12 <- fitGMVAR(data, p=1, M=2, maxit=5)
fit12

# Iterate more:
fit12_2 <- iterate_more(fit12)
fit12_2


# GMVAR(2,2) model with autoregressive parameters restricted
# to be the same for all regimes, only 10 iterations of the
# variable metric algorithm
C_mat <- rbind(diag(2*2^2), diag(2*2^2))
fit22c <- fitGMVAR(data, p=2, M=2, constraints=C_mat, maxit=10)
fit22c

# Iterate more:
fit22c_2 <- iterate_more(fit22c)
fit22c_2

# GMVAR(3,2) model, only 10 iterations of the variable metric
# algorithm
fit32 <- fitGMVAR(data, p=3, M=2, maxit=10)
fit32

# Iterate more:
fit32_2 <- iterate_more(fit32)
fit32_2
# }