| specify_starting_values_bsvar_sv {bsvars} | R Documentation |
The class StartingValuesBSVAR-SV presents starting values for the bsvar model with Stochastic Volatility heteroskedasticity.
bsvars::StartingValuesBSVAR -> StartingValuesBSVAR-SV
Aan NxK matrix of starting values for the parameter A.
Ban NxN matrix of starting values for the parameter B.
hypera 5-vector of starting values for the shrinkage hyper-parameters of the hierarchical prior distribution.
han NxT matrix with the starting values of the log-volatility processes.
rhoan N-vector with values of SV autoregressive parameters.
omegaan N-vector with values of SV process conditional standard deviations.
San NxT integer matrix with the auxiliary mixture component indicators.
sigma2_omegaan N-vector with variances of the zero-mean normal prior for \omega_n.
s_a positive scalar with the scale of the gamma prior of the hierarchical prior for \sigma^2_{\omega}.
new()Create new starting values StartingValuesBSVAR-SV.
specify_starting_values_bsvar_sv$new(N, p, T)
Na positive integer - the number of dependent variables in the model.
pa positive integer - the autoregressive lag order of the SVAR model.
Ta positive integer - the the time series dimension of the dependent variable matrix Y.
Starting values StartingValuesBSVAR-SV.
get_starting_values()Returns the elements of the starting values StartingValuesBSVAR-SV as a list.
specify_starting_values_bsvar_sv$get_starting_values()
# starting values for a bsvar model with 1 lag for a 3-variable system sv = specify_starting_values_bsvar_sv$new(N = 3, p = 1, T = 100) sv$get_starting_values() # show starting values as list
set_starting_values()Returns the elements of the starting values StartingValuesBSVAR_SV as a list.
specify_starting_values_bsvar_sv$set_starting_values(last_draw)
last_drawa list containing the last draw of the current MCMC run.
An object of class StartingValuesBSVAR including the last draw of the current MCMC as the starting value to be passed to the continuation of the MCMC estimation using bsvar().
# starting values for a bsvar model with 1 lag for a 3-variable system sv = specify_starting_values_bsvar_sv$new(N = 3, p = 1, T = 100) # Modify the starting values by: sv_list = sv$get_starting_values() # getting them as list sv_list$A <- matrix(rnorm(12), 3, 4) # modifying the entry sv$set_starting_values(sv_list) # providing to the class object
clone()The objects of this class are cloneable with this method.
specify_starting_values_bsvar_sv$clone(deep = FALSE)
deepWhether to make a deep clone.
# starting values for a bsvar model for a 3-variable system
sv = specify_starting_values_bsvar_sv$new(N = 3, p = 1, T = 100)
## ------------------------------------------------
## Method `specify_starting_values_bsvar_sv$get_starting_values`
## ------------------------------------------------
# starting values for a bsvar model with 1 lag for a 3-variable system
sv = specify_starting_values_bsvar_sv$new(N = 3, p = 1, T = 100)
sv$get_starting_values() # show starting values as list
## ------------------------------------------------
## Method `specify_starting_values_bsvar_sv$set_starting_values`
## ------------------------------------------------
# starting values for a bsvar model with 1 lag for a 3-variable system
sv = specify_starting_values_bsvar_sv$new(N = 3, p = 1, T = 100)
# Modify the starting values by:
sv_list = sv$get_starting_values() # getting them as list
sv_list$A <- matrix(rnorm(12), 3, 4) # modifying the entry
sv$set_starting_values(sv_list) # providing to the class object