Compute log of marginal likelihood of Bayesian Fit
compute_logml(object, ...)# S3 method for bvarmn
compute_logml(object, ...)
# S3 method for bvharmn
compute_logml(object, ...)
log likelihood of Minnesota prior model.
Model fit
not used
Closed form of Marginal Likelihood of BVAR can be derived by
$$p(Y_0) = \pi^{-mn / 2} \frac{\Gamma_m ((\alpha_0 + n) / 2)}{\Gamma_m (\alpha_0 / 2)} \det(\Omega_0)^{-m / 2} \det(S_0)^{\alpha_0 / 2} \det(\hat{V})^{- m / 2} \det(\hat{\Sigma}_e)^{-(\alpha_0 + n) / 2}$$
Closed form of Marginal Likelihood of BVHAR can be derived by
$$p(Y_0) = \pi^{-ms_0 / 2} \frac{\Gamma_m ((d_0 + n) / 2)}{\Gamma_m (d_0 / 2)} \det(P_0)^{-m / 2} \det(U_0)^{d_0 / 2} \det(\hat{V}_{HAR})^{- m / 2} \det(\hat{\Sigma}_e)^{-(d_0 + n) / 2}$$
Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior Selection for Vector Autoregressions. Review of Economics and Statistics, 97(2).