draw_posterior.dfmodel: Posterior Simulation

In bvartools: Bayesian Inference of Vector Autoregressive and Error Correction Models

Posterior Simulation

Description

Forwards model input to posterior simulation functions.

Usage

## S3 method for class 'dfmodel'
draw_posterior(object, FUN = NULL, mc.cores = NULL, ...)

Arguments

object	a list of model specifications, which should be passed on to function FUN. Usually, the output of a call to gen_var, gen_vec or gen_dfm in combination with add_priors.
FUN	the function to be applied to each list element in argument object. If NULL (default), the internal functions bvarpost is used for VAR model, bvecpost for VEC models and dfmpost for dynamic factor models.
mc.cores	the number of cores to use, i.e. at most how many child processes will be run simultaneously. The option is initialized from environment variable MC_CORES if set. Must be at least one, and parallelization requires at least two cores.
...	further arguments passed to or from other methods.

Value

For multiple models a list of objects of class bvarlist. For a single model the object has the class of the output of the applied posterior simulation function. In case the package's own functions are used, this will be "bvar", "bvec" or "dfm".

Examples

# Load data 
data("e1")
e1 <- diff(log(e1)) * 100

# Generate model
model <- gen_var(e1, p = 1:2, deterministic = 2,
                 iterations = 100, burnin = 10)
# Chosen number of iterations and burn-in should be much higher.

# Add priors
model <- add_priors(model)

# Obtain posterior draws
object <- draw_posterior(model)

bvartools documentation built on Aug. 31, 2023, 1:09 a.m.