gmvarkit (version 1.2.0)

print_std_errors: Print standard errors of GMVAR model in the same form as the model estimates are printed

Description

print_std_errors prints the approximate standard errors of GMVAR model in the same form as the parameters of objects of class 'gmvar' are printed.

Usage

print_std_errors(gmvar, digits = 3)

Arguments

gmvar

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

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 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 Unpublished Revision of HECER Discussion Paper No. 247.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. 2020. Structural Gaussian mixture vector autoregressive model. Unpublished working paper, available as arXiv:2007.04713.

See Also

profile_logliks, fitGMVAR, GMVAR, print.gmvar, swap_parametrization

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 with default settings
fit12 <- fitGMVAR(data, p=1, M=2)
fit12
print_std_errors(fit12)

# GMVAR(2,2) model with mean parametrization
fit22 <- fitGMVAR(data, p=2, M=2, parametrization="mean")
fit22
print_std_errors(fit22)

# GMVAR(2,2) model with autoregressive parameters restricted
# to be the same for all regimes
C_mat <- rbind(diag(2*2^2), diag(2*2^2))
fit22c <- fitGMVAR(data, p=2, M=2, constraints=C_mat)
fit22c
print_std_errors(fit22c)

# GMVAR(2,2) model with autoregressive parameters restricted
# to be the same for all regimes and non-diagonl elements
# the coefficient matrices constrained to zero.
tmp <- matrix(c(1, rep(0, 10), 1, rep(0, 8), 1, rep(0, 10), 1),
 nrow=2*2^2, byrow=FALSE)
C_mat2 <- rbind(tmp, tmp)
fit22c2 <- fitGMVAR(data, p=2, M=2, constraints=C_mat2, ncalls=10)
fit22c2
print_std_errors(fit22c2)
# }