gmvarkit (version 1.1.1)

swap_parametrization: Swap the parametrization of object of class 'gmvar' defining a GMVAR model

Description

swap_parametrization swaps the parametrization of object of class 'gmvar' to "mean" if the current parametrization is "intercept", and vice versa.

Usage

swap_parametrization(gmvar)

Arguments

gmvar

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

Value

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

Remark that the first autocovariance/correlation matrix in $uncond_moments is for the lag zero, the second one for the lag one, etc.

Details

swap_parametrization is convenient tool if you have estimated the model in "intercept"-parametrization, but wish to work with "mean"-parametrization in the future, or vice versa. In gmvarkit, for example the approximate standard errors are only available for parametrized parameters.

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 Unpublished Revision of HECER Discussion Paper No. 247.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, iterate_more

Examples

# NOT RUN {
# 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), d=2 model:
params122 <- c(0.623, -0.129, 0.959, 0.089, -0.006, 1.006, 1.746,
 0.804, 5.804, 3.245, 7.913, 0.952, -0.037, -0.019, 0.943, 6.926,
 3.982, 12.135, 0.789)
mod122 <- GMVAR(data, p=1, M=2, params=params122)
mod122 # intercept-parametrization

mod122_2 <- swap_parametrization(mod122)
mod122_2 # mean-parametrization


# GMVAR(2,2), d=2 model:
params222 <- c(-11.904, 154.684, 1.314, 0.145, 0.094, 1.292, -0.389,
 -0.070, -0.109, -0.281, 0.920, -0.025, 4.839, 11.633, 124.983, 1.248,
  0.077, -0.040, 1.266, -0.272, -0.074, 0.034, -0.313, 5.855, 3.570,
  9.838, 0.740)
mod222 <- GMVAR(data, p=2, M=2, params=params222, parametrization="mean")
mod222 # mean-parametrization

mod222_2 <- swap_parametrization(mod222)
mod222_2 # intercept-parametrization
# }