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Nowcasting and aggregation: Why small Euro area countries matter

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  • Andrii Babii
  • Luca Barbaglia
  • Eric Ghysels
  • Jonas Striaukas

Abstract

The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data. Using a panel of 19 Euro area countries, we investigate whether directly nowcasting the Euro area aggregate is better than weighted individual country nowcasts. Our results highlight the importance of the information from small- and medium-sized countries, particularly when including the COVID-19 pandemic period. The empirical analysis is supplemented by studying the so-called Big Four -- France, Germany, Italy, and Spain -- and the value added of news data when official statistics are lagging. From a theoretical perspective, we formally show that the aggregation of individual components forecasted with pooled panel data regressions is superior to direct aggregate forecasting due to lower estimation error.

Suggested Citation

  • Andrii Babii & Luca Barbaglia & Eric Ghysels & Jonas Striaukas, 2025. "Nowcasting and aggregation: Why small Euro area countries matter," Papers 2509.24780, arXiv.org.
    • Handle: RePEc:arx:papers:2509.24780
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    References listed on IDEAS

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    1. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
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