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Nowcasting GDP using machine learning algorithms: A real-time assessment

Citations

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Cited by:

  1. is not listed on IDEAS
  2. Ofori, Isaac Kwesi, 2021. "Catching The Drivers of Inclusive Growth in Sub-Saharan Africa: An Application of Machine Learning," EconStor Preprints 235482, ZBW - Leibniz Information Centre for Economics.
  3. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2024. "Lessons from nowcasting GDP across the world," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 8, pages 187-217, Edward Elgar Publishing.
  4. Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Amancio, Diego R., 2024. "Machine learning and economic forecasting: The role of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
  5. Jan Schulz & Kerstin Hötte & Daniel M. Mayerhoffer, 2024. "Pluralist economics in an era of polycrisis," Review of Evolutionary Political Economy, Springer, vol. 5(2), pages 201-218, September.
  6. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
  7. Zin Mar Oo & Ching‐Yang Lin & Makoto Kakinaka, 2025. "Deciphering Long‐Term Economic Growth: An Exploration With Leading Machine Learning Techniques," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1531-1562, July.
  8. Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2024. "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from the Lasso Regularization and Inferential Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 144-179, March.
  9. Alexander Eliseev, 2025. "Nowcasting Russian GDP in a Mixed-Frequency DSGE Model with a Panel of Non-Modelled Variables," Russian Journal of Money and Finance, Bank of Russia, vol. 84(3), pages 63-93, September.
  10. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
  11. Juan Jos√© Rinc√≥n Brice√±o, 2025. "Colombian economic activity nowcasting: addressing nonlinearities and high dimensionality through machine-learning," Documentos CEDE 21388, Universidad de los Andes, Facultad de Economía, CEDE.
  12. Huaqing Xie & Xingcheng Xu & Fangjia Yan & Xun Qian & Yanqing Yang, 2024. "Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact," Papers 2409.02551, arXiv.org.
  13. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
  14. Yanqing Yang & Xingcheng Xu & Jinfeng Ge & Yan Xu, 2024. "Machine Learning for Economic Forecasting: An Application to China's GDP Growth," Papers 2407.03595, arXiv.org.
  15. Kristian Jönsson, 2024. "Neighbor Weighting and Distance Metrics in Nearest Neighbor Nowcasting of Swedish GDP," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(4), pages 1077-1089, December.
  16. Alina Landowska & Robert A. K{l}opotek & Dariusz Filip & Konrad Raczkowski, 2025. "GDP-GFCF Dynamics Across Global Economies: A Comparative Study of Panel Regressions and Random Forest," Papers 2504.20993, arXiv.org.
  17. repec:ehl:lserod:128852 is not listed on IDEAS
  18. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
  19. Thierry Warin & Sarah Elimam, 2025. "GDP 5.0: Real-Time, Micro-Founded and Sustainable Metrics for Beyond-GDP Economic Assessment," CIRANO Working Papers 2025s-20, CIRANO.
  20. Grechyna, Daryna & Ofori, Pamela Efua, 2025. "Robust determinants of green finance," Energy Economics, Elsevier, vol. 152(C).
  21. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
  22. Samuel N. Cohen & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Arthur Turrell & Lingyi Yang, 2023. "Nowcasting using regression on signatures," Papers 2305.10256, arXiv.org, revised Dec 2025.
  23. 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.
  24. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
  25. Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
  26. 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.
  27. Kaustubh, Kaustubh & Ranjan, Abhishek, 2025. "A multi-factor GDP nowcast model for India," Economic Modelling, Elsevier, vol. 147(C).
  28. Manu Sharma & Vinish Kathuria, 2025. "Macroeconomic Nowcasting: What can Central Banks Learn from a Structured Literature Review?," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 23(2), pages 333-388, June.
  29. Jairo Flores & Bruno Gonzaga & Walter Ruelas-Huanca & Juan Tang, 2025. "Nowcasting Peru's GDP with Machine Learning Methods," IHEID Working Papers 01-2025, Economics Section, The Graduate Institute of International Studies.
  30. Rodrigo Moreira & Larissa Ferreira Rodrigues Moreira & Flávio Oliveira Silva, 2025. "Brazilian Selic Rate Forecasting with Deep Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1319-1339, March.
  31. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
  32. Seo, Beomseok, 2025. "Econometric forecasting using ubiquitous news text: Text-enhanced factor model," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1055-1072.
  33. Daniel Hopp, 2024. "Benchmarking econometric and machine learning methodologies in nowcasting GDP," Empirical Economics, Springer, vol. 66(5), pages 2191-2247, May.
  34. Kakuho Furukawa & Ryohei Hisano, 2022. "A Nowcasting Model of Exports Using Maritime Big Data," Bank of Japan Working Paper Series 22-E-19, Bank of Japan.
  35. Wishnu Badrawani, 2025. "An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia," Papers 2506.10369, arXiv.org.
  36. Daniel Musafiri Balungu & Avinash Kumar, 2024. "Forecasting The Economic Growth of Sverdlovsk Region: A Comparative Analysis of Machine Learning, Linear Regression and Autoregressive Models," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(3), pages 674-695.
  37. 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.
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