bvarpost {bvartools}

Posterior Simulation for BVAR Models

Description

Produces draws from the posterior distributions of Bayesian VAR models.

Usage

bvarpost(object)

Arguments

object

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

Details

The function implements commonly used posterior simulation algorithms for Bayesian VAR models with both constant and time varying parameters (TVP) as well as stochastic volatility. It can produce posterior draws for standard BVAR models with independent normal-Wishart priors, which can be augmented by stochastic search variable selection (SSVS) as proposed by Geroge et al. (2008) or Bayesian variable selection (BVS) as proposed in Korobilis (2013). Both SSVS or BVS can also be applied to the covariances of the error term.

The implementation follows the descriptions in Chan et al. (2019), George et al. (2008) and Korobilis (2013). For all approaches the SUR form of a VAR model is used to obtain posterior draws. The algorithm is implemented in C++ to reduce calculation time.

The function also supports structural BVAR models, where the structural coefficients are estimated from contemporary endogenous variables, which corresponds to the so-called (A-model). Currently, only specifications are supported, where the structural matrix contains ones on its diagonal and all lower triangular elements are freely estimated. Since posterior draws are obtained based on the SUR form of the VAR model, the structural coefficients are drawn jointly with the other coefficients.

Value

An object of class "bvar".

References

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

George, E. I., Sun, D., & Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1), 553–580. doi:10.1016/j.jeconom.2007.08.017

Korobilis, D. (2013). VAR forecasting using Bayesian variable selection. Journal of Applied Econometrics, 28(2), 204–230. doi:10.1002/jae.1271

Examples

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

# Create model
model <- gen_var(e1, p = 2, deterministic = "const",
                 iterations = 50, burnin = 10)
# Number of iterations and burnin should be much higher.

# Add priors
model <- add_priors(model)

# Obtain posterior draws 
object <- bvarpost(model)