specify_starting_values_bsvar_hmsh: R6 Class Representing StartingValuesBSVAHMSH

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

R6 Class Representing StartingValuesBSVAHMSH

Description

The class StartingValuesBSVARHMSH presents starting values for the bsvar model with Heterogeneous Markov Switching Heteroskedasticity.

Super class

StartingValuesBSVAR -> StartingValuesBSVARHMSH

Public fields

A

an NxK matrix of starting values for the parameter A.

B

an NxN matrix of starting values for the parameter B.

hyper

a (2*N+1)x2 matrix of starting values for the shrinkage hyper-parameters of the hierarchical prior distribution.

sigma2

an NxM matrix of starting values for the MS state-specific variances of the structural shocks. Its elements sum to value M over the rows.

PR_TR

an MxMxN array of starting values for the transition probability matrix of the Markov process. Its elements sum to 1 over the rows.

xi

an MxTxN array of starting values for the Markov process indicator. Its columns are a chosen column of an identity matrix of order M.

pi_0

an MxN matrix of starting values for state probability at time t=0. Its elements sum to 1 in columns.

lambda

a NxT matrix of starting values for latent variables.

df

an Nx1 vector of positive numbers with starting values for the equation-specific degrees of freedom parameters of the Student-t conditional distribution of structural shocks.

Methods

Public methods

• StartingValuesBSVARHMSH$new()

• StartingValuesBSVARHMSH$get_starting_values()

• StartingValuesBSVARHMSH$set_starting_values()

• StartingValuesBSVARHMSH$clone()

---

StartingValuesBSVARHMSH$new()

Create new starting values StartingValuesBSVARHMSH.

Usage

StartingValuesBSVARHMSH$new(A, B, N, p, M, T, d = 0, finiteM = TRUE)

Arguments

A

a logical NxK matrix containing value TRUE for the elements of the autoregressive matrix A to be estimated and value FALSE for exclusion restrictions to be set to zero.

B

a logical NxN matrix containing value TRUE for the elements of the structural matrix B to be estimated and value FALSE for exclusion restrictions to be set to zero.

N

a positive integer - the number of dependent variables in the model.

p

a positive integer - the autoregressive lag order of the SVAR model.

M

an integer greater than 1 - the number of Markov process' heteroskedastic regimes.

T

a positive integer - the the time series dimension of the dependent variable matrix Y.

d

a positive integer - the number of exogenous variables in the model.

finiteM

a logical value - if true a stationary Markov switching model is estimated. Otherwise, a sparse Markov switching model is estimated in which M=20 and the number of visited states is estimated.

Returns

Starting values StartingValuesBSVARHMSH.

---

StartingValuesBSVARHMSH$get_starting_values()

Returns the elements of the starting values StartingValuesBSVARHMSH as a list.

Usage

StartingValuesBSVARHMSH$get_starting_values()

Examples

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_hmsh$new(A = A, B = B, N = 3, p = 1, M = 2, T = 100)
sv$get_starting_values()   # show starting values as list

---

StartingValuesBSVARHMSH$set_starting_values()

Returns the elements of the starting values StartingValuesBSVARHMSH as a list.

Usage

StartingValuesBSVARHMSH$set_starting_values(last_draw)

Arguments

last_draw

a list containing the last draw.

Returns

An object of class StartingValuesBSVARHMSH including the last draw of the current MCMC as the starting value to be passed to the continuation of the MCMC estimation using estimate().

Examples

# starting values for a bsvar model with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_hmsh$new(A = A, B = B, N = 3, p = 1, M = 2, 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

---

StartingValuesBSVARHMSH$clone()

The objects of this class are cloneable with this method.

Usage

StartingValuesBSVARHMSH$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# starting values for a bsvar model for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_hmsh$new(A = A, B = B, N = 3, p = 1, M = 2, T = 100)


## ------------------------------------------------
## Method `StartingValuesBSVARHMSH$get_starting_values()`
## ------------------------------------------------

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_hmsh$new(A = A, B = B, N = 3, p = 1, M = 2, T = 100)
sv$get_starting_values()   # show starting values as list


## ------------------------------------------------
## Method `StartingValuesBSVARHMSH$set_starting_values()`
## ------------------------------------------------

# starting values for a bsvar model with 1 lag for a 3-variable system
A = matrix(TRUE, 3, 4)
B = matrix(TRUE, 3, 3)
sv = specify_starting_values_bsvar_hmsh$new(A = A, B = B, N = 3, p = 1, M = 2, 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

bsvars documentation built on Aug. 22, 2026, 5:09 p.m.