specify_starting_values_bsvar: R6 Class Representing StartingValuesBSVAR

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

R6 Class Representing StartingValuesBSVAR

Description

The class StartingValuesBSVAR presents starting values for the homoskedastic bsvar model.

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.

Methods

Public methods

• specify_starting_values_bsvar$new()

• specify_starting_values_bsvar$get_starting_values()

• specify_starting_values_bsvar$set_starting_values()

• specify_starting_values_bsvar$clone()

---

Method new()

Create new starting values StartingValuesBSVAR.

Usage

specify_starting_values_bsvar$new(N, p, d = 0)

Arguments

N

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

p

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

d

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

Returns

Starting values StartingValuesBSVAR.

Examples

# starting values for a homoskedastic bsvar with 4 lags for a 3-variable system
sv = specify_starting_values_bsvar$new(N = 3, p = 4)

---

Method get_starting_values()

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

Usage

specify_starting_values_bsvar$get_starting_values()

Examples

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
sv = specify_starting_values_bsvar$new(N = 3, p = 1)
sv$get_starting_values()   # show starting values as list

---

Method set_starting_values()

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

Usage

specify_starting_values_bsvar$set_starting_values(last_draw)

Arguments

last_draw

a list containing the last draw of elements B - an NxN matrix, A - an NxK matrix, and hyper - a vector of 5 positive real numbers.

Returns

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 estimate().

Examples

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
sv = specify_starting_values_bsvar$new(N = 3, p = 1)

# 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

---

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_starting_values_bsvar$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# starting values for a homoskedastic bsvar for a 3-variable system
sv = specify_starting_values_bsvar$new(N = 3, p = 1)


## ------------------------------------------------
## Method `specify_starting_values_bsvar$new`
## ------------------------------------------------

# starting values for a homoskedastic bsvar with 4 lags for a 3-variable system
sv = specify_starting_values_bsvar$new(N = 3, p = 4)


## ------------------------------------------------
## Method `specify_starting_values_bsvar$get_starting_values`
## ------------------------------------------------

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
sv = specify_starting_values_bsvar$new(N = 3, p = 1)
sv$get_starting_values()   # show starting values as list


## ------------------------------------------------
## Method `specify_starting_values_bsvar$set_starting_values`
## ------------------------------------------------

# starting values for a homoskedastic bsvar with 1 lag for a 3-variable system
sv = specify_starting_values_bsvar$new(N = 3, p = 1)

# 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 Oct. 24, 2024, 5:11 p.m.