specify_prior_bsvar_sv: R6 Class Representing PriorBSVARSV
In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models
specify_prior_bsvar_sv R Documentation
R6 Class Representing PriorBSVARSV
Description
The class PriorBSVARSV presents a prior specification for the bsvar model with Stochastic Volatility heteroskedasticity.
Super class
bsvars::PriorBSVAR -> PriorBSVARSV
Public fields
Aan NxK matrix, the mean of the normal prior distribution for the parameter matrix A.
A_V_inva KxK precision matrix of the normal prior distribution for each of the row of the parameter matrix A. This precision matrix is equation invariant.
B_V_invan NxN precision matrix of the generalised-normal prior distribution for the structural matrix B. This precision matrix is equation invariant.
B_nua positive integer greater of equal than N, a shape parameter of the generalised-normal prior distribution for the structural matrix B.
hyper_nu_Ba positive scalar, the shape parameter of the inverted-gamma 2 prior
for the overall shrinkage parameter for matrix B.
hyper_a_Ba positive scalar, the shape parameter of the gamma prior
for the second-level hierarchy for the overall shrinkage parameter for matrix B.
hyper_s_BBa positive scalar, the scale parameter of the inverted-gamma 2 prior
for the third-level of hierarchy for overall shrinkage parameter for matrix B.
hyper_nu_BBa positive scalar, the shape parameter of the inverted-gamma 2 prior
for the third-level of hierarchy for overall shrinkage parameter for matrix B.
hyper_nu_Aa positive scalar, the shape parameter of the inverted-gamma 2 prior
for the overall shrinkage parameter for matrix A.
hyper_a_Aa positive scalar, the shape parameter of the gamma prior
for the second-level hierarchy for the overall shrinkage parameter for matrix A.
hyper_s_AAa positive scalar, the scale parameter of the inverted-gamma 2 prior
for the third-level of hierarchy for overall shrinkage parameter for matrix A.
hyper_nu_AAa positive scalar, the shape parameter of the inverted-gamma 2 prior
for the third-level of hierarchy for overall shrinkage parameter for matrix A.
sv_a_a positive scalar, the shape parameter of the gamma prior in the hierarchical prior for \sigma^2_{\omega}.
sv_s_a positive scalar, the scale parameter of the gamma prior in the hierarchical prior for \sigma^2_{\omega}.
Methods
Public methods
Method new()
Create a new prior specification PriorBSVARSV.
Usage
specify_prior_bsvar_sv$new(N, p, d = 0, stationary = rep(FALSE, N))
Arguments
Na positive integer - the number of dependent variables in the model.
pa positive integer - the autoregressive lag order of the SVAR model.
da positive integer - the number of exogenous variables in the model.
stationaryan N logical vector - its element set to FALSE sets the prior mean for the autoregressive parameters of the Nth equation to the white noise process, otherwise to random walk.
Returns
A new prior specification PriorBSVARSV.
Method get_prior()
Returns the elements of the prior specification PriorBSVARSV as a list.
Usage
specify_prior_bsvar_sv$get_prior()
Examples
# a prior for 3-variable example with four lags
prior = specify_prior_bsvar_sv$new(N = 3, p = 4)
prior$get_prior() # show the prior as list
Method clone()
The objects of this class are cloneable with this method.
Usage
specify_prior_bsvar_sv$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
prior = specify_prior_bsvar_sv$new(N = 3, p = 1) # a prior for 3-variable example with one lag
prior$A # show autoregressive prior mean
## ------------------------------------------------
## Method `specify_prior_bsvar_sv$get_prior`
## ------------------------------------------------
# a prior for 3-variable example with four lags
prior = specify_prior_bsvar_sv$new(N = 3, p = 4)
prior$get_prior() # show the prior as list
bsvars documentation built on Oct. 24, 2024, 5:11 p.m.
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| specify_prior_bsvar_sv | R Documentation |
R6 Class Representing PriorBSVARSV
Description
The class PriorBSVARSV presents a prior specification for the bsvar model with Stochastic Volatility heteroskedasticity.
Super class
bsvars::PriorBSVAR -> PriorBSVARSV
Public fields
Aan
NxKmatrix, the mean of the normal prior distribution for the parameter matrixA.A_V_inva
KxKprecision matrix of the normal prior distribution for each of the row of the parameter matrixA. This precision matrix is equation invariant.B_V_invan
NxNprecision matrix of the generalised-normal prior distribution for the structural matrixB. This precision matrix is equation invariant.B_nua positive integer greater of equal than
N, a shape parameter of the generalised-normal prior distribution for the structural matrixB.hyper_nu_Ba positive scalar, the shape parameter of the inverted-gamma 2 prior for the overall shrinkage parameter for matrix
B.hyper_a_Ba positive scalar, the shape parameter of the gamma prior for the second-level hierarchy for the overall shrinkage parameter for matrix
B.hyper_s_BBa positive scalar, the scale parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix
B.hyper_nu_BBa positive scalar, the shape parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix
B.hyper_nu_Aa positive scalar, the shape parameter of the inverted-gamma 2 prior for the overall shrinkage parameter for matrix
A.hyper_a_Aa positive scalar, the shape parameter of the gamma prior for the second-level hierarchy for the overall shrinkage parameter for matrix
A.hyper_s_AAa positive scalar, the scale parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix
A.hyper_nu_AAa positive scalar, the shape parameter of the inverted-gamma 2 prior for the third-level of hierarchy for overall shrinkage parameter for matrix
A.sv_a_a positive scalar, the shape parameter of the gamma prior in the hierarchical prior for
\sigma^2_{\omega}.sv_s_a positive scalar, the scale parameter of the gamma prior in the hierarchical prior for
\sigma^2_{\omega}.
Methods
Public methods
Method new()
Create a new prior specification PriorBSVARSV.
Usage
specify_prior_bsvar_sv$new(N, p, d = 0, stationary = rep(FALSE, N))
Arguments
Na positive integer - the number of dependent variables in the model.
pa positive integer - the autoregressive lag order of the SVAR model.
da positive integer - the number of
exogenousvariables in the model.stationaryan
Nlogical vector - its element set toFALSEsets the prior mean for the autoregressive parameters of theNth equation to the white noise process, otherwise to random walk.
Returns
A new prior specification PriorBSVARSV.
Method get_prior()
Returns the elements of the prior specification PriorBSVARSV as a list.
Usage
specify_prior_bsvar_sv$get_prior()
Examples
# a prior for 3-variable example with four lags prior = specify_prior_bsvar_sv$new(N = 3, p = 4) prior$get_prior() # show the prior as list
Method clone()
The objects of this class are cloneable with this method.
Usage
specify_prior_bsvar_sv$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
prior = specify_prior_bsvar_sv$new(N = 3, p = 1) # a prior for 3-variable example with one lag
prior$A # show autoregressive prior mean
## ------------------------------------------------
## Method `specify_prior_bsvar_sv$get_prior`
## ------------------------------------------------
# a prior for 3-variable example with four lags
prior = specify_prior_bsvar_sv$new(N = 3, p = 4)
prior$get_prior() # show the prior as list
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