bvartools (version 0.2.4)

add_priors.dfmodel: Add Priors to Dynamic Factor Model

Description

Adds prior specifications to a list of models, which was produced by function gen_dfm.

Usage

# S3 method for dfmodel
add_priors(
  object,
  lambda = list(v_i = 0.01),
  sigma_u = list(shape = 5, rate = 4),
  a = list(v_i = 0.01),
  sigma_v = list(shape = 5, rate = 4),
  ...
)

Value

A list of models.

Arguments

object

a list, usually, the output of a call to gen_dfm.

lambda

a named list of prior specifications for the factor loadings in the measurement equation. For the default specification the diagonal elements of the inverse prior variance-covariance matrix are set to 0.01. The variances need to be specified as precisions, i.e. as inverses of the variances.

sigma_u

a named list of prior specifications for the error variance-covariance matrix. See 'Details'.

a

a named list of prior specifications for the coefficients of the transition equation. For the default specification the diagonal elements of the inverse prior variance-covariance matrix are set to 0.01. The variances need to be specified as precisions, i.e. as inverses of the variances.

sigma_v

a named list of prior specifications for the error variance-covariance matrix. See 'Details'.

...

further arguments passed to or from other methods.

Details

Argument lambda can only contain the element v_i, which is a numeric specifying the prior precision of the loading factors of the measurement equation. Default is 0.01.

The function assumes an inverse gamma prior for the errors of the measurement equation. Argument sigma_u can contain the following elements:

shape

a numeric or character specifying the prior shape parameter of the error terms of the measurement equation. Default is 5.

rate

a numeric specifying the prior rate parameter of the error terms of the measurement equation. Default is 4.

Argument a can only contain the element v_i, which is a numeric specifying the prior precision of the coefficients of the transition equation. Default is 0.01.

The function assumes an inverse gamma prior for the errors of the transition equation. Argument sigma_v can contain the following elements:

shape

a numeric or character specifying the prior shape parameter of the error terms of the transition equation. Default is 5.

rate

a numeric specifying the prior rate parameter of the error terms of the transition equation. Default is 4.

References

Chan, J., Koop, G., Poirier, D. J., & Tobias J. L. (2019). Bayesian econometric methods (2nd ed.). Cambridge: Cambridge University Press.

Lütkepohl, H. (2007). New introduction to multiple time series analysis (2nd ed.). Berlin: Springer.

Examples

# Load data
data("bem_dfmdata")

# Generate model data
model <- gen_dfm(x = bem_dfmdata, p = 1, n = 1,
                 iterations = 5000, burnin = 1000)
# Number of iterations and burnin should be much higher.

# Add prior specifications
model <- add_priors(model,
                    lambda = list(v_i = .01),
                    sigma_u = list(shape = 5, rate = 4),
                    a = list(v_i = .01),
                    sigma_v = list(shape = 5, rate = 4))