across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we …

notably with the pandemic. In a VAR, allowing the errors to have a distribution with fatter tails than the Gaussian one equips … the model to better deal with the COVID-19 shock. A standard Gaussian VAR can still be used for producing conditional …

relative to other predictors generally improving in the post-2012 period. An out-of-sample forecasting exercise indicates that …, when monetary and credit aggregates are loaded directly in the forecasting equation, the additional gains over the … and credit variables in forecasting inflation, even if their information content is diluted in a much broader pool of …

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

Monitoring economic conditions in real time, or nowcasting, is among the key tasks routinely performed by economists …. Nowcasting entails some key challenges, which also characterise modern Big Data analytics, often referred to as the three "Vs …

; (iv) newer generation Phillips curve models with several timevarying features are a promising avenue for forecasting …

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 …