swap_parametrization {gmvarkit}

Swap the parametrization of a GMVAR, StMVAR, or G-StMVAR model

Description

swap_parametrization swaps the parametrization of a GMVAR, StMVAR or G-StMVAR, model to "mean" if the current parametrization is "intercept", and vice versa.

Usage

swap_parametrization(gsmvar)

Arguments

gsmvar

an object of class 'gsmvar', typically created with fitGSMVAR or GSMVAR.

Details

swap_parametrization is a 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, the approximate standard errors are only available for parametrized parameters.

Value

Returns an object of class 'gsmvar' defining the specified reduced form or structural GMVAR, StMVAR, or G-StMVAR model. Can be used to work with other functions provided in gmvarkit.

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

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 Unpublished Revision of HECER Discussion Paper No. 247.

• Virolainen S. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.

• Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.

See Also

fitGSMVAR, GSMVAR, iterate_more, update_numtols

Examples

# GMVAR(2, 2), d=2 model with mean-parametrization:
params22 <- c(0.869, 0.549, 0.223, 0.059, -0.151, 0.395, 0.406,
 -0.005, 0.083, 0.299, 0.215, 0.002, 0.03, 0.576, 1.168, 0.218,
 0.02, -0.119, 0.722, 0.093, 0.032, 0.044, 0.191, 1.101, -0.004,
 0.105, 0.58)
mod22 <- GSMVAR(gdpdef, p=2, M=2, params=params22, parametrization="mean")
mod22 # mean parametrization

mod22_2 <- swap_parametrization(mod22)
mod22_2 # intercept parametrization

# G-StMVAR(2, 1, 1), d=2 model with mean-parametrization:
mod22gs <- GSMVAR(gdpdef, p=2, M=c(1, 1), params=c(params22, 10), model="G-StMVAR",
                  parametrization="mean")
mod22gs # mean parametrization

mod22gs_2 <- swap_parametrization(mod22gs)
mod22gs_2 # intercept parametrization

# Structural GMVAR(2, 2), d=2 model identified with sign-constraints:
params22s <- c(0.36, 0.121, 0.484, 0.072, 0.223, 0.059, -0.151, 0.395,
  0.406, -0.005, 0.083, 0.299, 0.218, 0.02, -0.119, 0.722, 0.093, 0.032,
  0.044, 0.191, 0.057, 0.172, -0.46, 0.016, 3.518, 5.154, 0.58)
W_22 <- matrix(c(1, 1, -1, 1), nrow=2, byrow=FALSE)
mod22s <- GSMVAR(p=2, M=2, d=2, params=params22s, structural_pars=list(W=W_22))
mod22s # intercept parametrization

mod22s_2 <- swap_parametrization(mod22s)
mod22s_2 # mean parametrization