as a pseudo real time forecasting exercise, i.e. due account is taken of the pattern of available monthly variables over …

Indices (PMI) in anticipating US real economic activity. We conduct a fully-fledged real-time out-ofsample forecasting … forecasting GDP growth, while it performs quite poorly in anticipating industrial production growth. Combining the information …

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 …

forecasting processes within central banks and similar institutions, that require forecasts to be conditional on a set of … alternative to existing approaches to conditional density forecasting, notably Bayesian VARs, with considerable advantages in … terms of flexibility and additional insights that do not come at the cost of forecasting performance. …

provides valid intuition on the merits of each approach. The forecasting performance of the models is also assessed in the …

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean square …