 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.
<clarity>  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.
 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.