Central banks

FEDS Paper: Beyond the Streetlight: Economic Measurement in the Division of Research and Statistics at the Federal Reserve

Carol Corrado, Arthur Kennickell, and Tomaz CajnerThis paper was written for the academic conference held in celebration of the 100th anniversary of the Division of Research and Statistics (R&S) of the Federal Reserve Board. The work of the Federal Reserve turns strongly on empirical efforts to understand the structure and state of the economy, and R&S can be thought of as operating a large factory for discovering and developing data and analytical methods to provide evidence relevant to the mission of the Board.

Using corporate earnings calls to forecast euro area labour demand

This box explores the use of corporate earnings calls as a novel, high-frequency source of data for nowcasting and forecasting labour demand in the euro area. Labour demand has started to show signs of cooling following its post-pandemic peak. By applying textual analysis to transcripts of earnings calls, we construct an indicator that correlates strongly with the euro area job vacancy rate. This metric enables us to produce timely forecasts ahead of official data releases. Utilising a mixed data sampling (MIDAS) regression approach, we use this indicator to forecast the job vacancy rate.

Insights from banks and firms on euro area credit conditions: a comparison based on ECB surveys

This box examines euro area credit conditions from the perspective of banks and firms. The analysis uses data from the bank lending survey and the survey on the access to finance of enterprises. By offering qualitative insights into credit supply and demand, these surveys complement hard data in analysing how monetary policy is transmitted to firms through banks. The respective survey findings confirm that the general economic outlook and firm-specific conditions are significant factors affecting credit standards and the availability of bank loans.

The increasing energy demand of artificial intelligence and its impact on commodity prices

The use of artificial intelligence (AI) models has grown rapidly in recent years. This box explores how these models could affect energy demand in the future. Over the period from 2022 to 2026, the AI-related rise in global electricity consumption is projected to equal around 4% of the EU’s total electricity consumption and is likely to be met by either natural gas power plants or renewables. While this increase is significant in absolute terms, it is expected to have a limited impact on gas prices given the vast size of global natural gas markets.

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