In this paper, we consider whether differences in the forecast performance of ECB SPF respondents reflect ability or chance. Although differences in performance metrics sometimes appear substantial, it is challenging to determine whether they reflect ex ante skill or other factors impacting ex...

This paper proposes a new methodology based on textual analysis to forecast U.S. recessions. Specifically, the paper develops an index in the spirit of Baker et al. (2016) and Caldara and Iacoviello (2018) which tracks developments in U.S. real activity. When used in a standard recession...

We show that financial variables contribute to the forecast of GDP growth during the Great Recession, providing additional insights on both first and higher moments of the GDP growth distribution. If a recession is due to an unforeseen shock (such as the Covid-19 recession), financial variables...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

We evaluate forecasts for the euro area in data-rich and 'data-lean' environments by comparing three different approaches: a simple PMI model based on Purchasing Managers’ Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data...

This paper shows that newspaper articles contain timely economic signals that can materially improve nowcasts of real GDP growth for the euro area. Our text data is drawn from fifteen popular European newspapers, that collectively represent the four largest Euro area economies, and are machine...

We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including...

This paper derives forecasts for euro area real GDP growth based on a bottom up approach from the production side. That is, GDP is forecast via the forecasts of value added across the different branches of activity, which is quite new in the literature. Linear regression models in the form of...

This paper investigates the importance of including data on new housing supply in Dynamic Stochastic General Equilibrium (DSGE) models in forecasting the Great Financial Crisis (GFC), focusing on the U.S. While existing models have added a financial sector and real estate sector, they have...