Before revision:  The Monte Carlo design considers several sparsity structures (sparse, dense, block) and error structures (i. i.d. errors and heteroskedastic errors). A number of LASSO based estimators (frequentist and Bayesian) are pitted against the HS-BQR to better gauge the performance of the method on the different designs. The HS-BQR yields just as good, or better performance than the other estimators considered when evaluated using coefficient bias and forecast error.
After revision:  The Monte Carlo design considers several sparsity structures (sparse, dense, block) and error structures . Compared to alternative shrinkage priors, the proposed HS-BQR yields just as good, or better performance than the other estimators considered when evaluated using coefficient bias and forecast error.