importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …

variables in the model, rather than just on future paths as it is usually done in the conditional forecasting literature. The … proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target … forecasting densities of a BVAR and a DSGE model on information about the marginal densities of future oil prices. The results …

forecasting, however it is robust in the sense that it provides a more accurate estimation of the predictive density in the region … simulate model parameters from the Partially Censored Posterior, and PCP-QERMit, an Importance Sampling method that is …

quickly tuned adaptive candidate, straightforward importance sampling provides a computationally efficient estimator of the …

forecast and econ omic gains. Similar forecast gains are obtained in point and density forecasting of US real GDP, Inflation …