the beginning of 2018. They also have performed well in forecasting the direction of inflation. In terms of the …

forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed …-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). We study the forecasting accuracy …

variables in the model, rather than just on future paths as it is usually done in the conditional forecasting literature. The … forecasting densities of a BVAR and a DSGE model on information about the marginal densities of future oil prices. The results …-inflation over the considered forecasting horizon. Finally, a real-time forecasting exercise yields that introducing market …

-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting performance. …

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 …