reorder_B_columns {sstvars}

Reorder columns of impact matrix B (and lambda parameters if any) of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity.

Description

reorder_B_columns reorder columns of impact matrix B (and lambda parameters if any) of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity.

Usage

reorder_B_columns(stvar, perm, 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.

perm

an integer vector of length d specifying the new order of the columns of the impact matrix. For model identified by...

heteroskedasticity

also lambda parameters of each regime will be reordered accordingly.

non-Gaussianity

the columns of the impact matrices of all the regimes and the component specific distribution parameters (degrees of freedom parameters) are reordered accordingly.

calc_std_errors

should approximate standard errors be calculated?

Details

The order of the columns of the impact matrix can be changed without changing the implied reduced form model (as long as, for models identified by heteroskedasticity, the order of lambda parameters is also changed accordingly; and for model identified by non-Gaussianity, ordering of the columns of all the impact matrices and the component specific distribution parameters is also changed accordingly). Note that constraints imposed on the impact matrix via B_constraints will also be modified accordingly.

Also all signs in any column of impact matrix can be swapped (without changing the implied reduced form model) with the function swap_B_signs. This obviously also swaps the sign constraints (if any) in the corresponding columns of the impact matrix.

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.

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, swap_B_signs

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

# Reverse the ordering of the columns of W (or equally the impact matrix):
mod222logt_rev <- reorder_B_columns(mod222logt, perm=c(2, 1))
mod222logt_rev # The columns of the impact matrix are in a reversed order

# Swap the ordering of the columns of the impact matrix back to the original:
mod222logt_rev2 <- reorder_B_columns(mod222logt_rev, perm=c(2, 1))
mod222logt_rev2 # The columns of the impact matrix are back in the original ordering

# Below code does not do anything, as perm=1:2, so the ordering does not change:
mod222logt3 <- reorder_B_columns(mod222logt, perm=c(1, 2))
mod222logt3 # The ordering of the columns did not change from the original