This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation … risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting … approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state …

that, in this case, adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR …

an out-of-sample forecasting exercise, the paper shows that the proposed approach performs well as compared to other well …

find satisfactory results in terms of forecasting, especially when looking at quarterly variables, such as employment …

point and density forecasts, in line with forecasting practices at many policy institutions. Our main findings are that … point forecasts perform similarly using both approaches, whereas directly forecasting aggregate indices tends to yield … better density forecasts. In the aftermath of the Great Financial Crisis, relative forecasting performance was typically only …