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A daily indicator of economic growth for the euro area

Author

Listed:
  • Valentina Aprigliano
  • Claudia Foroni
  • Massimiliano Marcellino
  • Gianluigi Mazzi
  • Fabrizio Venditti

Abstract

In this paper, we study alternative methods to construct a daily indicator of growth for the euro area. We aim for an indicator that (i) provides reliable predictions, (ii) can be easily updated at the daily frequency, (iii) gives interpretable signals, and (iv) it is linear. Using a large panel of daily and monthly data for the euro area we explore the performance of two classes of models: bridge and U-MIDAS models, and different forecast combination strategies. Forecasts obtained from U-MIDAS models, combined with the inverse MSE weights, best satisfy the required criteria.

Suggested Citation

  • Valentina Aprigliano & Claudia Foroni & Massimiliano Marcellino & Gianluigi Mazzi & Fabrizio Venditti, 2017. "A daily indicator of economic growth for the euro area," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 43-63.
    • Handle: RePEc:ids:ijcome:v:7:y:2017:i:1/2:p:43-63
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    Cited by:

    1. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    2. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    3. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.

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