print_std_errors {gmvarkit}

Print standard errors of a GMVAR, StMVAR, or G-StMVAR model in the same form as the model estimates are printed

Description

print_std_errors prints the approximate standard errors of a GMVAR, StMVAR, or G-StMVAR model in the same form as the parameters of objects of class 'gsmvar' are printed.

Usage

print_std_errors(gsmvar, digits = 3)

Arguments

gsmvar

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

digits

how many digits should be printed?

Details

The main purpose of print_std_errors is to provide a convenient tool to match the standard errors to certain parameter estimates. Note that if the model is intercept parametrized, there won't be standard errors for the unconditional means, and vice versa. Also, there is no standard error for the last mixing weight alpha_M because it is not parametrized.

Note that if linear constraints are imposed and they involve summations or multiplications, then the AR parameter standard errors are printed separately as they don't correspond one-to-one to the model parameter standard errors.

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 Unpublished Revision of HECER Discussion Paper No. 247.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 Unpublished working paper, available as arXiv:2109.13648.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

profile_logliks, fitGSMVAR, GSMVAR, print.gsmvar, swap_parametrization

Examples

# GMVAR(1,2) model
fit12 <- fitGSMVAR(gdpdef, p=1, M=2, ncalls=1, seeds=1)
fit12
print_std_errors(fit12)