reorder_B_columns() R function from [sstvars]

Reorder columns of impact matrix B of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity.

reorder_B_columns reorder columns of impact matrix B of a structural STVAR model that is identified by heteroskedasticity or non-Gaussianity. For models identified by heteroskedasticity, also the lambda parameters are reordered accordingly. For models with ind Student or skewed t shocks, also the degrees of freedom and skewness parameters (latter for skewed t) are reordered accordingly.


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 and skewness parameters) are reordered accordingly.
calc_std_errors: should approximate standard errors be calculated?

Returns

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 $(\phi_{1,0},...,\phi_{M,0},\varphi_1,...,\varphi_M,\alpha$ (intercepts replaced by unconditional means if mean parametrization is used), if applicable.

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, as long as signs of any skewness parameters are also swapped accordingly) with the function swap_B_signs. This obviously also swaps the sign constraints (if any) in the corresponding columns of the impact matrix.

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

References

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

reorder_B_columns function