bvartools (version 0.2.4)

bvecpost: Posterior Simulation for BVEC Models

Description

Produces draws from the posterior distributions of Bayesian VEC models.

Usage

bvecpost(object)

Value

An object of class "bvec".

Arguments

object

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

Details

The function implements posterior simulation algorithms proposed in Koop et al. (2010) and Koop et al. (2011), which place identifying restrictions on the cointegration space. Both algorithms are able to employ Bayesian variable selection (BVS) as proposed in Korobilis (2013). The algorithm of Koop et al. (2010) is also able to employ stochastic search variable selection (SSVS) as proposed by Geroge et al. (2008). 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). Chan et al. (2019), George et al. (2008) and Korobilis (2013) were used to implement the 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 VEC model, the structural coefficients are drawn jointly with the other coefficients. No identifying restrictions 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. tools:::Rd_expr_doi("10.1016/j.jeconom.2007.08.017")

Koop, G., León-González, R., & Strachan R. W. (2010). Efficient posterior simulation for cointegrated models with priors on the cointegration space. Econometric Reviews, 29(2), 224--242. tools:::Rd_expr_doi("10.1080/07474930903382208")

Koop, G., León-González, R., & Strachan R. W. (2011). Bayesian inference in a time varying cointegration model. Journal of Econometrics, 165(2), 210--220. tools:::Rd_expr_doi("10.1016/j.jeconom.2011.07.007")

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

Examples

# 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)