Documents Saiz, Lorena 2 results

"The sudden and deep recession triggered by the outbreak of the coronavirus (COVID-19) has warranted adjusting the standard tools used for forecasting euro area real GDP growth in real time. The severe economic consequences of COVID-19 have played havoc with established statistical and economic relationships.[1] Hence, standard short-term forecasting models have been able to capture neither the extent of the contraction observed in the first two quarters of 2020 – with quarter-on-quarter declines of 3.7% and 11.7% in the first and the second quarters, respectively – nor the rebound in the third quarter – with an increase of 12.5%. These exceptional dynamics have required an update of the set of tools typically used to forecast euro area real GDP growth in real time. This box describes four approaches developed by ECB staff to account for the specific characteristics and implications of the COVID-19 pandemic..."

"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 translated into English. Daily sentiment metrics are created from these news articles and we assess their value for nowcasting. By comparing to competitive and rigorous benchmarks, we find that newspaper text is helpful in nowcasting GDP growth especially in the first half of the quarter when other lower-frequency soft indicators are not available. The choice of the sentiment measure matters when tracking economic shocks such as the Great Recession and the Great Lockdown. Non-linear machine learning models can help capture extreme movements in growth, but require sufficient training data in order to be effective so become more useful later in our sample."