bvartools (version 0.1.0)

bvecpost: Posterior Simulation for BVEC Models

Description

Produces draws from the posterior distributions of Bayesian VEC models.

Usage

bvecpost(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 a posterior simulation algorithm, which places identifying restrictions on the cointegration space. Th algorithm is also able to employ stochastic search variable selection (SSVS) as proposed by Geroge et al. (2008) or Bayesian variable selection (BVS) as proposed in Korobilis (2013). Both SSVS and BVS can also be applied to the covariances of the error term. However, the algorithms cannot be applied to cointegration related coefficients, i.e. to the loading matrix \(\alpha\) or the cointegration matrix \(beta\).

The implementation primarily follows the description in Koop et al. (2010). However, Chan et al. (2019), George et al. (2008) and Korobilis (2013) were used to implement variable selection algorithms. For all approaches the SUR form of a VEC model is used to obtain posterior draws. The algorithm is implemented in C++ to reduce calculation time.

The function also supports structural BVEC 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. No identifying restrictionare are made regarding the cointegration matrix.

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. https://doi.org/10.1016/j.jeconom.2007.08.017

Koop, G., Le<U+00F3>n-Gonz<U+00E1>lez, R., & Strachan R. W. (2010). Efficient posterior simulation for cointegrated models with priors on the cointegration space. Econometric Reviews, 29(2), 224--242. https://doi.org/10.1080/07474930903382208

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

Examples

# NOT RUN {
# Get data
data("e6")

# Create model
model <- gen_vec(e6, p = 4, r = 1,
                 const = "unrestricted", seasonal = "unrestricted",
                 iterations = 100, burnin = 10)
# Chosen number of iterations and burnin should be much higher.

# Add priors
model <- add_priors(model)

# Obtain posterior draws 
object <- bvecpost(model)

# }