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R6 Class Representing StartingValuesBVAR

Source: R/specify_bvar.R
specify_starting_values_bvar.Rd

The class StartingValuesBVAR presents starting values for the BVAR model.

Public fields

A

an NxK matrix of starting values for the autoregressive matrix \(A\).

Sigma

an NxN matrix of starting values for the error term covariance \(\Sigma\).

V

a KxK matrix of starting values for the prior equation-specific covariance \(V\) of the hierarchical prior distribution for matrix \(A\).

h

an T-vector with the starting values of the log-volatility processes.

rho

a scalalr for the SV autoregressive parameter.

omega

a scalar for the SV process conditional standard deviation.

sigma2v

a scalar for SV process conditional variances.

S

a T integer vector with the auxiliary mixture component indicator.

sigma2_omega

a scalar for the variance of the zero-mean normal prior for \(\omega\).

s_

a positive scalar with the scale of the gamma prior of the hierarchical prior for \(\sigma^2_{\omega}\).

lambda

a T-vetor of starting values for latent variable.

df

a scalar greater than 2 with the starting value for the degrees of freedom parameter of the Student-t conditional distribution of error term.

Methods

Public methods

Method new()

Create new starting values StartingValuesBVAR.

Usage

specify_starting_values_bvar$new(
  N,
  p,
  T,
  d = 0,
  is_homoskedastic = TRUE,
  is_normal = TRUE,
  ar_sigma2 = rep(1, N),
  kappa = c(0.2^2, 10^2)
)

Arguments

N

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

p

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

T

a positive integer - the number of time periods in the data.

d

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

is_homoskedastic

a logical scalar - if TRUE the model assumes homoskedastic errors, otherwise it assumes stochastic volatility.

is_normal

a logical scalar - if TRUE the model assumes normal error term, otherwise, it assumes Student-t errors.

ar_sigma2

a positive N-vector with the autoregressive variance estimates for each variable to be used in the Minnesota prior for the autoregressive parameters.

kappa

a positive 2-vector with the hyperparameters of the Minnesota prior for the autoregressive parameters - the first element is the overall tightness hyperparameter, while the second element is the tightness of the prior on the constant and exogenous variable coefficients.

Returns

Starting values StartingValuesBVAR.

Examples

# starting values for a 3-variable BVAR model
sv = specify_starting_values_bvar$new(N = 3, p = 4, T = 100)

Method get_starting_values()

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

Usage

specify_starting_values_bvar$get_starting_values()

Examples

# starting values for a 3-variable BVAR model
sv = specify_starting_values_bvar$new(N = 3, p = 4, T = 100)
sv$get_starting_values()   # show starting values as list

Method set_starting_values()

Sets the elements of the starting values StartingValuesBVAR to provided values.

Usage

specify_starting_values_bvar$set_starting_values(last_draw)

Arguments

last_draw

a list containing the last draw of elements A - a KxN matrix, Sigma - an NxN matrix, and V - a KxK matrix.

Returns

An object of class StartingValuesBVAR 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 3-variable BVAR model
sv = specify_starting_values_bvar$new(N = 3, p = 4, 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

Method clone()

The objects of this class are cloneable with this method.

Usage

specify_starting_values_bvar$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# starting values for a 3-variable BVAR model.
sv = specify_starting_values_bvar$new(N = 3, p = 4, T = 100)


## ------------------------------------------------
## Method `specify_starting_values_bvar$new`
## ------------------------------------------------

# starting values for a 3-variable BVAR model
sv = specify_starting_values_bvar$new(N = 3, p = 4, T = 100)


## ------------------------------------------------
## Method `specify_starting_values_bvar$get_starting_values`
## ------------------------------------------------

# starting values for a 3-variable BVAR model
sv = specify_starting_values_bvar$new(N = 3, p = 4, T = 100)
sv$get_starting_values()   # show starting values as list
#> $A
#>           [,1]      [,2]        [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
#> [1,] 0.5741127 0.0000000 0.000000000    0    0    0    0    0    0     0     0
#> [2,] 0.0000000 0.2358854 0.000000000    0    0    0    0    0    0     0     0
#> [3,] 0.0000000 0.0000000 0.007971651    0    0    0    0    0    0     0     0
#>      [,12] [,13]
#> [1,]     0     0
#> [2,]     0     0
#> [3,]     0     0
#> 
#> $Sigma
#>           [,1]       [,2]      [,3]
#> [1,] 0.2874713 0.00000000 0.0000000
#> [2,] 0.0000000 0.02791551 0.0000000
#> [3,] 0.0000000 0.00000000 0.2883005
#> 
#> $V
#>       [,1] [,2] [,3] [,4] [,5] [,6]        [,7]        [,8]        [,9]  [,10]
#>  [1,] 0.04 0.00 0.00 0.00 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0000
#>  [2,] 0.00 0.04 0.00 0.00 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0000
#>  [3,] 0.00 0.00 0.04 0.00 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0000
#>  [4,] 0.00 0.00 0.00 0.01 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0000
#>  [5,] 0.00 0.00 0.00 0.00 0.01 0.00 0.000000000 0.000000000 0.000000000 0.0000
#>  [6,] 0.00 0.00 0.00 0.00 0.00 0.01 0.000000000 0.000000000 0.000000000 0.0000
#>  [7,] 0.00 0.00 0.00 0.00 0.00 0.00 0.004444444 0.000000000 0.000000000 0.0000
#>  [8,] 0.00 0.00 0.00 0.00 0.00 0.00 0.000000000 0.004444444 0.000000000 0.0000
#>  [9,] 0.00 0.00 0.00 0.00 0.00 0.00 0.000000000 0.000000000 0.004444444 0.0000
#> [10,] 0.00 0.00 0.00 0.00 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0025
#> [11,] 0.00 0.00 0.00 0.00 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0000
#> [12,] 0.00 0.00 0.00 0.00 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0000
#> [13,] 0.00 0.00 0.00 0.00 0.00 0.00 0.000000000 0.000000000 0.000000000 0.0000
#>        [,11]  [,12] [,13]
#>  [1,] 0.0000 0.0000     0
#>  [2,] 0.0000 0.0000     0
#>  [3,] 0.0000 0.0000     0
#>  [4,] 0.0000 0.0000     0
#>  [5,] 0.0000 0.0000     0
#>  [6,] 0.0000 0.0000     0
#>  [7,] 0.0000 0.0000     0
#>  [8,] 0.0000 0.0000     0
#>  [9,] 0.0000 0.0000     0
#> [10,] 0.0000 0.0000     0
#> [11,] 0.0025 0.0000     0
#> [12,] 0.0000 0.0025     0
#> [13,] 0.0000 0.0000   100
#> 
#> $h
#>   [1]  1.696982e-03  3.001271e-03  1.109257e-02 -7.198301e-03  1.879593e-02
#>   [6] -4.177957e-03 -3.569055e-03 -1.859122e-03  9.036993e-03  1.007237e-02
#>  [11] -2.823084e-03  4.330427e-03  9.793907e-03 -2.217763e-03  1.629873e-04
#>  [16] -4.195563e-03  4.768723e-03 -5.340778e-03  1.492208e-02 -2.096973e-03
#>  [21] -1.526562e-03  9.234436e-03  2.052924e-02  4.052080e-03  2.081010e-02
#>  [26] -4.068178e-03  4.924050e-03 -2.678083e-04  2.221764e-02 -2.791158e-03
#>  [31]  2.705488e-02  4.442011e-03  4.657068e-04 -4.619567e-03 -7.104474e-03
#>  [36] -2.275246e-04 -5.306322e-03  5.466885e-03  1.074639e-02 -2.673047e-06
#>  [41]  2.035244e-03  4.382628e-03 -1.304181e-02  3.076142e-03  2.019408e-03
#>  [46]  9.527296e-03  5.320749e-03 -5.603359e-03  1.005445e-02 -3.508077e-03
#>  [51] -4.302493e-03 -1.206231e-02  1.394537e-02  6.091125e-03  9.837046e-03
#>  [56]  8.522565e-04 -5.298947e-03  3.313622e-02 -1.016320e-02 -1.201424e-02
#>  [61]  1.277549e-02 -2.384533e-03  8.251479e-03 -1.434804e-02  3.887690e-03
#>  [66] -1.706339e-02 -4.080955e-03 -8.146259e-03  3.395826e-03 -7.657576e-04
#>  [71] -1.406630e-03 -4.892756e-03  2.250724e-03  1.017381e-02  8.156466e-03
#>  [76]  4.473520e-05  1.244433e-02 -4.920910e-03  8.982512e-03  6.146918e-03
#>  [81] -4.003597e-03  9.680298e-05  6.290504e-03  1.133209e-02 -2.520703e-03
#>  [86]  2.116707e-03  5.049962e-03  3.973690e-03  7.809080e-03 -4.416303e-03
#>  [91] -5.216638e-03  1.013842e-02  5.594613e-04 -1.436022e-02 -5.100833e-04
#>  [96]  1.898591e-02 -1.569562e-02 -2.370156e-02 -3.606970e-03 -5.684785e-04
#> 
#> $rho
#> [1] 0.5
#> 
#> $omega
#> [1] 0.1
#> 
#> $sigma2v
#> [1] 0.01
#> 
#> $S
#>   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 
#> $sigma2_omega
#> [1] 1
#> 
#> $s_
#> [1] 0.05
#> 
#> $lambda
#>   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> 
#> $df
#> [1] 30
#> 


## ------------------------------------------------
## Method `specify_starting_values_bvar$set_starting_values`
## ------------------------------------------------

# starting values for a 3-variable BVAR model
sv = specify_starting_values_bvar$new(N = 3, p = 4, 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