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Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach

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  1. Labib Shami & Teddy Lazebnik, 2024. "Implementing Machine Learning Methods in Estimating the Size of the Non-observed Economy," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1459-1476, April.
  2. repec:nrb:journl:v:35:y:2023:i:1:p:22 is not listed on IDEAS
  3. Xiao-Shan Chen & Min Gyeong Kim & Chi-Ho Lin & Hyung Jong Na, 2025. "Development of per Capita GDP Forecasting Model Using Deep Learning: Including Consumer Goods Index and Unemployment Rate," Sustainability, MDPI, vol. 17(3), pages 1-28, January.
  4. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.
  5. Hamdy Ahmad Aly Alhendawy & Mohammed Galal Abdallah Mostafa & Mohamed Ibrahim Elgohari & Ibrahim Abdalla Abdelraouf Mohamed & Nabil Medhat Arafat Mahmoud & Mohamed Ahmed Mohamed Mater, 2023. "Determinants of Renewable Energy Production in Egypt New Approach: Machine Learning Algorithms," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 679-689, November.
  6. Miriam Hortas-Rico & Vicente Rios, 2023. "Does women’s political empowerment matter for income inequality?," Working Papers 2023-10, FEDEA.
  7. João Felix & Michel Alexandre & Gilberto Tadeu Lima, 2025. "Applying Machine Learning Algorithms to Predict the Size of the Informal Economy," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1169-1189, March.
  8. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
  9. Teddy Lazebnik & Tzach Fleischer & Amit Yaniv-Rosenfeld, 2023. "Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks," Sustainability, MDPI, vol. 15(14), pages 1-9, July.
  10. Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  11. Seyed Farshid Ghorashi & Maziyar Bahri & Atousa Goodarzi, 2024. "Developing and comparing machine learning approaches for predicting insurance penetration rates based on each country," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-29, December.
  12. Qingwen Li & Guangxi Yan & Chengming Yu, 2022. "A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
  13. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
  14. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  15. Sakiru Adebola Solarin & Muhammed Sehid Gorus & Onder Ozgur, 2024. "Modelling the economic effect of inbound birth tourism: a random forest algorithm approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4223-4240, October.
  16. Dennis W. Campbell & Ruidi Shang, 2022. "Tone at the Bottom: Measuring Corporate Misconduct Risk from the Text of Employee Reviews," Management Science, INFORMS, vol. 68(9), pages 7034-7053, September.
  17. Guilherme Schultz Lindenmeyer & Hudson Silva Torrent, 2024. "Boosting and Predictability of Macroeconomic Variables: Evidence from Brazil," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 377-409, July.
  18. Ramaharo, Franck Maminirina & Rasolofomanana, Gerzhino H, 2023. "Nowcasting Madagascar's real GDP using machine learning algorithms," AfricArxiv vpuac, Center for Open Science.
  19. Muhammed Sehid Gorus & Erdal Tanas Karagol, 2023. "Factors affecting per capita ecological footprint in OECD countries: Evidence from machine learning techniquesa," Energy & Environment, , vol. 34(7), pages 2601-2618, November.
  20. Dietrich, Stephan & Meysonnat, Aline & Rosales, Francisco & Cebotari, Victor & Gassmann, Franziska, 2021. "Economic development, weather shocks and child marriage in South Asia: A machine learning approach," MERIT Working Papers 2021-034, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
  21. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
  22. Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
  23. Robert-Paul Berben & Rajni Rasiawan & Jasper de Winter, 2025. "Forecasting Dutch inflation using machine learning methods," Working Papers 828, DNB.
  24. Ajit Desai, 2023. "Machine Learning for Economics Research: When What and How?," Papers 2304.00086, arXiv.org, revised Apr 2023.
  25. Teddy Lazebnik, 2025. "Going a Step Deeper Down the Rabbit Hole: Deep Learning Model to Measure the Size of the Unregistered Economy Activity," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1759-1774, March.
  26. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
  27. Muhammad Anees Khan & Kumail Abbas & Mazliham Mohd Su’ud & Anas A. Salameh & Muhammad Mansoor Alam & Nida Aman & Mehreen Mehreen & Amin Jan & Nik Alif Amri Bin Nik Hashim & Roslizawati Che Aziz, 2022. "Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
  28. 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.
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