dfmpost: Posterior Simulation for Dynamic Factor Models

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

Posterior Simulation for Dynamic Factor Models

Description

Produces draws from the posterior distributions of Bayesian dynamic factor models.

Usage

dfmpost(object)

Arguments

object

an object of class "dfmodel", usually, a result of a call to gen_dfm in combination with add_priors.

Details

The function implements the posterior simulation algorithm for Bayesian dynamic factor models.

The implementation follows the description in Chan et al. (2019) and C++ is used to reduce calculation time.

Value

An object of class "dfm".

References

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

Examples

# Load data
data("bem_dfmdata")

# Generate model data
model <- gen_dfm(x = bem_dfmdata, p = 1, n = 1,
                 iterations = 20, burnin = 10)
# 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))

# Obtain posterior draws
object <- dfmpost(model)

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