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R6 Class Representing PriorBSVAR

Source: R/specify.R
specify_prior_bsvarSIGN.Rd

The class PriorBSVARSIGN presents a prior specification for the homoskedastic bsvar model.

Public fields

p

a positive integer - the number of lags.

hyper

a (N+3)xS matrix of hyper-parameters \(\mu, \delta, \lambda, \psi\).

A

a NxK normal prior mean matrix for the autoregressive parameters.

V

a KxK matrix determining the normal prior column-specific covariance for the autoregressive parameters.

S

an NxN matrix determining the inverted-Wishart prior scale of error terms covariance matrix.

nu

a positive scalar greater than N+1 - the shape of the inverted-Wishart prior for error terms covariance matrix.

data

an TxN matrix of observations.

Y

an NxT matrix of dependent variables.

X

an KxT matrix of independent variables.

Ysoc

an NxN matrix with the sum-of-coefficients dummy observations.

Xsoc

an KxN matrix with the sum-of-coefficients dummy observations.

Ysur

an NxN matrix with the single-unit-root dummy observations.

Xsur

an KxN matrix with the single-unit-root dummy observations.

mu.scale

a positive scalar - the shape of the gamma prior for \(\mu\).

mu.shape

a positive scalar - the shape of the gamma prior for \(\mu\).

delta.scale

a positive scalar - the shape of the gamma prior for \(\delta\).

delta.shape

a positive scalar - the shape of the gamma prior for \(\delta\).

lambda.scale

a positive scalar - the shape of the gamma prior for \(\lambda\).

lambda.shape

a positive scalar - the shape of the gamma prior for \(\lambda\).

psi.scale

a positive scalar - the shape of the inverted gamma prior for \(\psi\).

psi.shape

a positive scalar - the shape of the inverted gamma prior for \(\psi\).

Methods

Public methods

Method new()

Create a new prior specification PriorBSVAR.

Usage

specify_prior_bsvarSIGN$new(
  data,
  p,
  exogenous = NULL,
  stationary = rep(FALSE, ncol(data))
)

Arguments

data

the TxN data matrix of observations.

p

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

exogenous

a Txd 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.

Returns

A new prior specification PriorBSVARSIGN.

Examples

# a prior for 5-variable example with one lag and stationary data
data(optimism)
prior = specify_prior_bsvarSIGN$new(optimism, p = 1)
prior$B # show autoregressive prior mean

Method get_prior()

Returns the elements of the prior specification PriorBSVAR as a list.

Usage

specify_prior_bsvarSIGN$get_prior()

Examples

# a prior for 5-variable example with four lags
prior = specify_prior_bsvar$new(N = 5, p = 4)
prior$get_prior() # show the prior as list

Method estimate_hyper()

Estimates hyper-parameters with adaptive Metropolis algorithm.

Usage

specify_prior_bsvarSIGN$estimate_hyper(
  S = 10000,
  burn_in = S/2,
  mu = FALSE,
  delta = FALSE,
  lambda = TRUE,
  psi = FALSE
)

Arguments

S

number of MCMC draws.

burn_in

number of burn-in draws.

mu

whether to estimate the hyper-parameter in the sum-of-coefficients dummy prior.

delta

whether to estimate the hyper-parameter in the single-unit-root dummy prior.

lambda

whether to estimate the hyper-parameter of the shrinkage in the Minnesota prior.

psi

whether to estimate the hyper-parameter of the variances in the Minnesota prior.

Examples

# specify the model and set seed
set.seed(123)
data(optimism)
prior = specify_prior_bsvarSIGN$new(optimism, p = 4)

# estimate hyper parameters with adaptive Metropolis algorithm
prior$estimate_hyper(S = 10, psi = TRUE)

# trace plot
hyper = t(prior$hyper)
colnames(hyper) = c("mu", "delta", "lambda", paste("psi", 1:5, sep = ""))
plot.ts(hyper)

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_prior_bsvarSIGN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# a prior for 5-variable example with one lag
data(optimism)
prior = specify_prior_bsvarSIGN$new(optimism, p = 1)
prior$A  # show autoregressive prior mean
#>      [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,]    1    0    0    0    0    0
#> [2,]    0    1    0    0    0    0
#> [3,]    0    0    1    0    0    0
#> [4,]    0    0    0    1    0    0
#> [5,]    0    0    0    0    1    0


## ------------------------------------------------
## Method `specify_prior_bsvarSIGN$new`
## ------------------------------------------------

# a prior for 5-variable example with one lag and stationary data
data(optimism)
prior = specify_prior_bsvarSIGN$new(optimism, p = 1)
prior$B # show autoregressive prior mean
#> NULL


## ------------------------------------------------
## Method `specify_prior_bsvarSIGN$get_prior`
## ------------------------------------------------

# a prior for 5-variable example with four lags
prior = specify_prior_bsvar$new(N = 5, p = 4)
prior$get_prior() # show the prior as list
#> $A
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,]    1    0    0    0    0    0    0    0    0     0     0     0     0     0
#> [2,]    0    1    0    0    0    0    0    0    0     0     0     0     0     0
#> [3,]    0    0    1    0    0    0    0    0    0     0     0     0     0     0
#> [4,]    0    0    0    1    0    0    0    0    0     0     0     0     0     0
#> [5,]    0    0    0    0    1    0    0    0    0     0     0     0     0     0
#>      [,15] [,16] [,17] [,18] [,19] [,20] [,21]
#> [1,]     0     0     0     0     0     0     0
#> [2,]     0     0     0     0     0     0     0
#> [3,]     0     0     0     0     0     0     0
#> [4,]     0     0     0     0     0     0     0
#> [5,]     0     0     0     0     0     0     0
#> 
#> $A_V_inv
#>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#>  [1,]    1    0    0    0    0    0    0    0    0     0     0     0     0
#>  [2,]    0    1    0    0    0    0    0    0    0     0     0     0     0
#>  [3,]    0    0    1    0    0    0    0    0    0     0     0     0     0
#>  [4,]    0    0    0    1    0    0    0    0    0     0     0     0     0
#>  [5,]    0    0    0    0    1    0    0    0    0     0     0     0     0
#>  [6,]    0    0    0    0    0    4    0    0    0     0     0     0     0
#>  [7,]    0    0    0    0    0    0    4    0    0     0     0     0     0
#>  [8,]    0    0    0    0    0    0    0    4    0     0     0     0     0
#>  [9,]    0    0    0    0    0    0    0    0    4     0     0     0     0
#> [10,]    0    0    0    0    0    0    0    0    0     4     0     0     0
#> [11,]    0    0    0    0    0    0    0    0    0     0     9     0     0
#> [12,]    0    0    0    0    0    0    0    0    0     0     0     9     0
#> [13,]    0    0    0    0    0    0    0    0    0     0     0     0     9
#> [14,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#> [15,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#> [16,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#> [17,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#> [18,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#> [19,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#> [20,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#> [21,]    0    0    0    0    0    0    0    0    0     0     0     0     0
#>       [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21]
#>  [1,]     0     0     0     0     0     0     0     0
#>  [2,]     0     0     0     0     0     0     0     0
#>  [3,]     0     0     0     0     0     0     0     0
#>  [4,]     0     0     0     0     0     0     0     0
#>  [5,]     0     0     0     0     0     0     0     0
#>  [6,]     0     0     0     0     0     0     0     0
#>  [7,]     0     0     0     0     0     0     0     0
#>  [8,]     0     0     0     0     0     0     0     0
#>  [9,]     0     0     0     0     0     0     0     0
#> [10,]     0     0     0     0     0     0     0     0
#> [11,]     0     0     0     0     0     0     0     0
#> [12,]     0     0     0     0     0     0     0     0
#> [13,]     0     0     0     0     0     0     0     0
#> [14,]     9     0     0     0     0     0     0     0
#> [15,]     0     9     0     0     0     0     0     0
#> [16,]     0     0    16     0     0     0     0     0
#> [17,]     0     0     0    16     0     0     0     0
#> [18,]     0     0     0     0    16     0     0     0
#> [19,]     0     0     0     0     0    16     0     0
#> [20,]     0     0     0     0     0     0    16     0
#> [21,]     0     0     0     0     0     0     0     1
#> 
#> $B_V_inv
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    0    0    0    0
#> [2,]    0    1    0    0    0
#> [3,]    0    0    1    0    0
#> [4,]    0    0    0    1    0
#> [5,]    0    0    0    0    1
#> 
#> $B_nu
#> [1] 5
#> 
#> $hyper_nu_B
#> [1] 10
#> 
#> $hyper_a_B
#> [1] 10
#> 
#> $hyper_s_BB
#> [1] 100
#> 
#> $hyper_nu_BB
#> [1] 1
#> 
#> $hyper_nu_A
#> [1] 10
#> 
#> $hyper_a_A
#> [1] 10
#> 
#> $hyper_s_AA
#> [1] 10
#> 
#> $hyper_nu_AA
#> [1] 10
#> 


## ------------------------------------------------
## Method `specify_prior_bsvarSIGN$estimate_hyper`
## ------------------------------------------------

# specify the model and set seed
set.seed(123)
data(optimism)
prior = specify_prior_bsvarSIGN$new(optimism, p = 4)

# estimate hyper parameters with adaptive Metropolis algorithm
prior$estimate_hyper(S = 10, psi = TRUE)
#> **************************************************|
#>  Adaptive Metropolis MCMC: hyper parameters       |
#> **************************************************|

# trace plot
hyper = t(prior$hyper)
colnames(hyper) = c("mu", "delta", "lambda", paste("psi", 1:5, sep = ""))
plot.ts(hyper)