Temporal aggregation in general introduces a moving average (MA) component in the aggregated model. A similar feature emerges when not all but only a few variables are aggregated, which generates a mixed frequency model. The MA component is generally neglected, likely to preserve the possibility...

We propose a new method for medium-term forecasting using exogenous information. We first show how a shifting-mean autoregressive model can be used to describe characteristic features in inflation series. This implies that we decompose the inflation process into a slowly moving nonstationary...

In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

Factor based forecasting has been at the forefront of developments in the macroeconometric forecasting literature in the recent past. Despite the flurry of activity in the area, a number of specification issues such as the choice of the number of factors in the forecasting regression, the...

Forecasting the world economy is a difficult task given the complex interrelationships within and across countries. This paper proposes a number of approaches to forecast short-term changes in selected world economic variables and aims, first, at ranking various forecasting methods in terms of...

The paper focuses on the estimation of the euro area output gap. We construct model-averaged measures of the output gap in order to cope with both model uncertainty and parameter instability that are inherent to trend-cycle decomposition models of GDP. We first estimate nine models of...

We document the impact of COVID-19 on frequently employed time series models, with a focus on euro area in ation. We show that for both single equation models (Phillips curves) and Vector Autoregressions (VARs) estimated parameters change notably with the pandemic. In a VAR, allowing the errors...

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced...

We compare direct forecasts of HICP and HICP excluding energy and food in the euro area and five member countries to aggregated forecasts of their main components from large Bayesian VARs with a shared set of predictors. We focus on conditional point and density forecasts, in line with...

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": the large number of time series continuously...