swap_B_signs() R function from [sstvars]

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

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.


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?

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

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.

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

References

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

swap_B_signs function