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Nowcasting world GDP growth with high‐frequency data

Citations

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Cited by:

  1. Jan Radovan & Igor Masten, 2025. "Nowcasting economic activity in a small open CESEE economy using mixed frequency data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 52(4), pages 721-776, November.
  2. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
  3. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
  4. Jiawen Luo & Jingyi Deng & Juncal Cunado & Rangan Gupta, 2025. "Forecasting GDP with Oil Price Shocks: A Mixed-Frequency Time-Varying Perspective," Working Papers 202523, University of Pretoria, Department of Economics.
  5. Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo, 2024. "Reservoir computing for macroeconomic forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1206-1237.
  6. Bricongne, Jean-Charles & Meunier, Baptiste & Macalos, Joao & Milis, Julia & Pical, Thomas, 2026. "Can satellites predict oil demand?," Working Paper Series 3198, European Central Bank.
  7. d'Aspremont, Alexandre & Ben Arous, Simon & Bricongne, Jean-Charles & Lietti, Benjamin & Meunier, Baptiste, 2025. "Satellites turn “concrete”: Tracking cement with satellite data and neural networks," Journal of Econometrics, Elsevier, vol. 249(PC).
  8. Dennis Kant & Andreas Pick & Jasper de Winter, 2025. "Nowcasting GDP using machine learning methods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(1), pages 1-24, March.
  9. Robert Lehmann & Sascha Möhrle, 2024. "Forecasting regional industrial production with novel high‐frequency electricity consumption data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1918-1935, September.
  10. Michael Anthonisz, 2023. "Nowcasting Key Australian Macroeconomic Variables," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(3), pages 371-380, September.
  11. Natalia Makeeva, 2025. "The impact of the official statistics revision on the accuracy of the Russian macroeconomic indicators nowcasting models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 79, pages 27-49.
  12. Satoshi Urasawa, 2023. "The Usefulness of High-Frequency Alternative Data to Obtain Nowcasts for Japan’s GDP: Evidence from Credit Card Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 191-211, September.
  13. Dalia Atif, 2025. "Enhancing Long-Term GDP Forecasting with Advanced Hybrid Models: A Comparative Study of ARIMA-LSTM and ARIMA-TCN with Dense Regression," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3447-3473, June.
  14. Luke Hartigan & Tom Rosewall, 2025. "Nowcasting Quarterly GDP Growth During the COVID‐19 Crisis Using a Monthly Activity Indicator," The Economic Record, The Economic Society of Australia, vol. 101(335), pages 456-484, December.
  15. Pradeep Mishra & Khder Alakkari & Mostafa Abotaleb & Pankaj Kumar Singh & Shilpi Singh & Monika Ray & Soumitra Sankar Das & Umme Habibah Rahman & Ali J. Othman & Nazirya Alexandrovna Ibragimova & Gulf, 2021. "Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)," Data, MDPI, vol. 6(11), pages 1-15, November.
  16. Ivan Stankevich, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
  17. A. Vamsikrishna & E. V. Gijo, 2024. "New Techniques to Perform Cross-Validation for Time Series Models," SN Operations Research Forum, Springer, vol. 5(2), pages 1-12, June.
  18. Chatelais, Nicolas & Stalla-Bourdillon, Arthur & Chinn, Menzie D., 2023. "Forecasting real activity using cross-sectoral stock market information," Journal of International Money and Finance, Elsevier, vol. 131(C).
  19. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
  20. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
  21. Nicolas Chatelais & Arthur Stalla-Bourdillon & Menzie D. Chinn, 2022. "Macroeconomic Forecasting using Filtered Signals from a Stock Market Cross Section," NBER Working Papers 30305, National Bureau of Economic Research, Inc.
  22. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
  23. Ivan Stankevich, 2025. "Nowcasting and short-term forecasting of G-20 countries GDP with endogenous regime-switching MIDAS models," Empirical Economics, Springer, vol. 69(3), pages 1383-1410, September.
  24. Cheng Wang & Mengnan Xu & Zheng Wang & Wenjing Sun, 2024. "Research on China insurance demand forecasting: Based on mixed frequency data model," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-16, July.
  25. Shuqin Zhang & Zhuoya Li & Lijiao Jing & Xinmin Li, 2025. "Nowcasting Monthly Chinese GDP with Mixed Frequency Data: A Model Averaging Approach," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 4493-4511, December.
  26. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023. "Testing big data in a big crisis: Nowcasting under Covid-19," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.
  27. Philip Rademacher, 2025. "Forecasting Recessions in Germany with Feature Selection and Machine Learning," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 21(2), pages 119-157, December.
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