specify_bsvar_msh: R6 Class representing the specification of the BSVAR model...

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

R6 Class representing the specification of the BSVAR model with Markov Switching Heteroskedasticity.

Description

The class BSVARMSH presents complete specification for the BSVAR model with Markov Switching Heteroskedasticity.

Public fields

p

a non-negative integer specifying the autoregressive lag order of the model.

identification

an object IdentificationBSVARs with the identifying restrictions.

prior

an object PriorBSVARMSH with the prior specification.

data_matrices

an object DataMatricesBSVAR with the data matrices.

starting_values

an object StartingValuesBSVARMSH with the starting values.

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.

Methods

Public methods

• specify_bsvar_msh$new()

• specify_bsvar_msh$get_data_matrices()

• specify_bsvar_msh$get_identification()

• specify_bsvar_msh$get_prior()

• specify_bsvar_msh$get_starting_values()

• specify_bsvar_msh$clone()

---

Method new()

Create a new specification of the BSVAR model with Markov Switching Heteroskedasticity, BSVARMSH.

Usage

specify_bsvar_msh$new(
  data,
  p = 1L,
  M = 2L,
  B,
  exogenous = NULL,
  stationary = rep(FALSE, ncol(data)),
  finiteM = TRUE
)

Arguments

data

a (T+p)xN matrix with time series data.

p

a positive integer providing model's autoregressive lag order.

M

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

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.

exogenous

a (T+p)xd matrix of exogenous variables.

stationary

an 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.

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

A new complete specification for the bsvar model with Markov Switching Heteroskedasticity, BSVARMSH.

---

Method get_data_matrices()

Returns the data matrices as the DataMatricesBSVAR object.

Usage

specify_bsvar_msh$get_data_matrices()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_data_matrices()

---

Method get_identification()

Returns the identifying restrictions as the IdentificationBSVARs object.

Usage

specify_bsvar_msh$get_identification()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_identification()

---

Method get_prior()

Returns the prior specification as the PriorBSVARMSH object.

Usage

specify_bsvar_msh$get_prior()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_prior()

---

Method get_starting_values()

Returns the starting values as the StartingValuesBSVARMSH object.

Usage

specify_bsvar_msh$get_starting_values()

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_starting_values()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_bsvar_msh$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

estimate, specify_posterior_bsvar_msh

Examples

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)


## ------------------------------------------------
## Method `specify_bsvar_msh$get_data_matrices`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_data_matrices()


## ------------------------------------------------
## Method `specify_bsvar_msh$get_identification`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_identification()


## ------------------------------------------------
## Method `specify_bsvar_msh$get_prior`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_prior()


## ------------------------------------------------
## Method `specify_bsvar_msh$get_starting_values`
## ------------------------------------------------

data(us_fiscal_lsuw)
spec = specify_bsvar_msh$new(
   data = us_fiscal_lsuw,
   p = 4,
   M = 2
)
spec$get_starting_values()

bsvars documentation built on Oct. 24, 2024, 5:11 p.m.