swap_B_signs: Swap all signs in pointed columns of the impact matrix of a...

In sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Swap all signs in pointed columns of the impact matrix of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity

Description

swap_B_signs swaps all signs in pointed columns of the impact matrix of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity. For ind skewed t models, also the signs of the skewness parameters are swapped accordingly.

Usage

swap_B_signs(stvar, which_to_swap, calc_std_errors = FALSE)

Arguments

stvar	a class 'stvar' object defining a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity, typically created with fitSSTVAR.
which_to_swap	a numeric vector of length at most d and elemnts in 1,..,d specifying the columns of the impact matrix whose sign should be swapped.
calc_std_errors	should approximate standard errors be calculated?

Details

All signs in any column of the impact matrix can be swapped without changing the implied reduced form model, but for ind skewed t models, also the signs of the corresponding skewness parameter values need to be swapped. For model identified by non-Gaussianity, the signs of the columns of the impact matrices of all the regimes are swapped accordingly. Note that the sign constraints imposed on the impact matrix via B_constraints are also swapped in the corresponding columns accordingly.

Also the order of the columns of the impact matrix can be changed (without changing the implied reduced form model) as long as the ordering of other related parameters is also changed accordingly. This can be done with the function reorder_B_columns.

Value

Returns an S3 object of class 'stvar' defining a smooth transition VAR model. The returned list contains the following components (some of which may be NULL depending on the use case):

data	The input time series data.
model	A list describing the model structure.
params	The parameters of the model.
std_errors	Approximate standard errors of the parameters, if calculated.
transition_weights	The transition weights of the model.
regime_cmeans	Conditional means of the regimes, if data is provided.
total_cmeans	Total conditional means of the model, if data is provided.
total_ccovs	Total conditional covariances of the model, if data is provided.
uncond_moments	A list of unconditional moments including regime autocovariances, variances, and means.
residuals_raw	Raw residuals, if data is provided.
residuals_std	Standardized residuals, if data is provided.
structural_shocks	Recovered structural shocks, if applicable.
loglik	Log-likelihood of the model, if data is provided.
IC	The values of the information criteria (AIC, HQIC, BIC) for the model, if data is provided.
all_estimates	The parameter estimates from all estimation rounds, if applicable.
all_logliks	The log-likelihood of the estimates from all estimation rounds, if applicable.
which_converged	Indicators of which estimation rounds converged, if applicable.
which_round	Indicators of which round of optimization each estimate belongs to, if applicable.
seeds	The seeds used in the estimation in fitSTVAR, if applicable.
LS_estimates	The least squares estimates of the parameters in the form (\phi_{1},...,\phi_{M},\varphi_1,...,\varphi_M,\alpha (intercepts replaced by unconditional means if mean parametrization is used), if applicable.

References

• Lütkepohl H., Netšunajev A. 2018. Structural vector autoregressions with smooth transition in variances. Journal of Economic Dynamics & Control, 84, 43-57.

See Also

GIRF, fitSSTVAR, reorder_B_columns

Examples

# Create a structural two-variate Student's t STVAR p=2, M=2, model with logistic transition
# weights and the first lag of the second variable as the switching variable, and shocks
# identified by heteroskedasticity:
theta_222logt <- c(0.356914, 0.107436, 0.356386, 0.086330, 0.139960, 0.035172, -0.164575,
  0.386816, 0.451675, 0.013086, 0.227882, 0.336084, 0.239257, 0.024173, -0.021209, 0.707502,
  0.063322, 0.027287, 0.009182, 0.197066, -0.03, 0.24, -0.76, -0.02, 3.36, 0.86, 0.1, 0.2, 7)
mod222logt <- STVAR(p=2, M=2, d=2, params=theta_222logt, weight_function="logistic",
  weightfun_pars=c(2, 1), cond_dist="Student", identification="heteroskedasticity")

# Print the parameter values, W and lambdas are printed in the bottom:
mod222logt

# Swap the signs of the first column of W (or equally the impact matrix):
mod222logt2 <- swap_B_signs(mod222logt, which_to_swap=1)
mod222logt2 # The signs of the first column of the impact matrix are swapped

# Swap the signs of the second column of the impact matrix:
mod222logt3 <- swap_B_signs(mod222logt, which_to_swap=2)
mod222logt3 # The signs of the second column of the impact matrix are swapped

# Swap the signs of both columns of the impact matrix:
mod222logt4 <- swap_B_signs(mod222logt, which_to_swap=1:2)
mod222logt4 # The signs of both columns of the impact matrix are swapped

sstvars documentation built on April 11, 2025, 5:47 p.m.