Research classified by Journal of Economic Literature (JEL) codes

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Top JEL
/ C: Mathematical and Quantitative Methods
/ / C4: Econometric and Statistical Methods: Special Topics
/ / / C45: Neural Networks and Related Topics

2026

• Tu DQ Le & Son H Tran & Thanh Ngo & Hung D Bui, 2026, "Forecasting Vietnam Inflation Using Machine Learning Approaches: A Comprehensive Analysis," Advances in Decision Sciences, Asia University, Taiwan, volume 30, issue 1, pages 136-185.
• Pascaline Dupas & Amy Handlan & Alicia Sasser Modestino & Muriel Niederle & Mateo Seré & Haoyu Sheng & Justin Wolfers & Seminar Dynamics Collective, 2026, "Gender Differences in Economics Seminars," American Economic Review, American Economic Association, volume 116, issue 2, pages 749-789, February, DOI: 10.1257/aer.20241718.
• Kevin A. Bryan, 2026, "The Economic Impacts of Artificial Intelligence: A Multidisciplinary, Multi-book Review," Journal of Economic Literature, American Economic Association, volume 64, issue 1, pages 281-300, March, DOI: 10.1257/jel.20251799.
• Robert Novy-Marx & Mihail Velikov, 2026, "Artificial Intelligence–Powered (Finance) Scholarship," Journal of Economic Literature, American Economic Association, volume 64, issue 1, pages 5-37, March, DOI: 10.1257/jel.20251821.
• Burke, Matt & Mohaddes, Kamiar & Raissi, Mehdi, 2026, "The Adaptation Imperative: Climate Change and Sovereign Credit Risk," INET Oxford Working Papers, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, number 2026-03, Feb.
• Benjamin Born & Nora Lamersdorf & Jana-Lynn Schuster & Sascha Steffen, 2026, "From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns," CRC TR 224 Discussion Paper Series, University of Bonn and University of Mannheim, Germany, number crctr224_2025_724, Jan.
• Burke, M. & Mohaddes, K & Raissi, M., 2026, "The Adaptation Imperative: Climate Change and Sovereign Credit Risk," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2608, Feb.
• Vegard H. Larsen & Leif Anders Thorsrud, 2026, "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," CESifo Working Paper Series, CESifo, number 12454.
• Bryan T. Kelly & Boris Kuznetsov & Semyon Malamud & Teng Andrea Xu & Yuan Zhang, 2026, "Large and Deep Factor Models," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 26-20, Feb.
• Laborda, Juan & Suárez, Cristina & Fernández, Alejandro & Wang, Haoran & Cerdá, Emilio & Ricci, Liana & Quiroga, Sonia, 2026, "Unveiling how financial markets could intensify climate change risks," Ecological Economics, Elsevier, volume 239, issue C, DOI: 10.1016/j.ecolecon.2025.108773.
• Goulet Coulombe, Philippe & Klieber, Karin, 2026, "An adaptive moving average for macroeconomic monitoring," Economics Letters, Elsevier, volume 259, issue C, DOI: 10.1016/j.econlet.2025.112773.
• Pascal, Julien, 2026, "A generalization of the Parameterized Expectations Algorithm," Economics Letters, Elsevier, volume 259, issue C, DOI: 10.1016/j.econlet.2025.112790.
• Bohórquez Correa, Santiago & Mosquera-López, Stephanía & Uribe, Jorge M., 2026, "Time-varying systemic risk in electricity markets using generative adversarial networks: Market resilience and policy," Energy Policy, Elsevier, volume 210, issue C, DOI: 10.1016/j.enpol.2025.115034.
• Zhou, Fan & Guo, Wenjing, 2026, "Time-varying network structure and volatility prediction in the cryptocurrency market," Finance Research Letters, Elsevier, volume 87, issue C, DOI: 10.1016/j.frl.2025.109028.
• Li, Boyan & Wu, Chongfeng, 2026, "Beyond delta neutrality: Confidence-scaled hedging with machine learning forecasts," Finance Research Letters, Elsevier, volume 87, issue C, DOI: 10.1016/j.frl.2025.109098.
• Fazekas, Mihály & Tóth, Bence & Wachs, Johannes & Abdou, Aly, 2026, "Public procurement cartels: A large-sample testing of screens using machine learning," International Journal of Industrial Organization, Elsevier, volume 104, issue C, DOI: 10.1016/j.ijindorg.2025.103228.
• Wang, Ziyang & Li, Yunpeng & Cui, Zhihao & Zheng, Weinan & Wang, Ting, 2026, "A machine learning-based study of credit risk in supply chain finance of listed service-oriented enterprises in China," Pacific-Basin Finance Journal, Elsevier, volume 96, issue C, DOI: 10.1016/j.pacfin.2025.103043.
• Mati, Sagiru & Usman, Abdullahi G. & Ismael, Goran Yousif & Babuga, Umar Tijjani & Nadarajah, Saralees & Masoud, Serag & Uzun Ozsahin, Dilber & Abba, Sani I., 2026, "Explainable support vector regression coupled with quantum firefly optimisation algorithm for carbon emission prediction in West Africa: The role of socioeconomic, energy, and environmental factors," Renewable Energy, Elsevier, volume 256, issue PE, DOI: 10.1016/j.renene.2025.124298.
• Xing, Xiaochao & Hong, Yanran & Wang, Lu, 2026, "A novel LSTM-based Granger-causality approach: A case study on traditional energy and stock markets," Renewable Energy, Elsevier, volume 256, issue PG, DOI: 10.1016/j.renene.2025.124519.
• Matt Burke & Kamiar Mohaddes & Mehdi Raissi, 2026, "The Adaptation Imperative: Climate Change and Sovereign Credit Risk," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2026-11, Feb.
• Marx Davidson Nkonyam Ando & Bartosz Zakrzewski & Irena Dul, 2026, "Artificial Intelligence in the Management of Transportation Companies," European Research Studies Journal, European Research Studies Journal, volume 0, issue 1, pages 432-447.
• M.Jahangir Alam & Shane Boyle & Huiyu Li & Tatevik Sekhposyan, 2026, "ChatMacro: Evaluating Inflation Forecasts of Generative AI," Working Paper Series, Federal Reserve Bank of San Francisco, number 2026-04, Feb, DOI: 10.24148/wp2026-04.
• Gautami Parate & Arpita Choudhary, 2026, "Patent Valuation under Fragile Institutional Enforcement: A Continuous-Time Markov Approach," Working Papers, Madras School of Economics,Chennai,India, number 2026-293, Jan.
• Hui Chen & Yuhan Cheng & Yanchu Liu & Ke Tang, 2026, "Teaching Economics to the Machines," NBER Working Papers, National Bureau of Economic Research, Inc, number 34713, Jan.
• Joshua S. Gans, 2026, "Optimal Use of Preferences in Artificial Intelligence Algorithms," NBER Working Papers, National Bureau of Economic Research, Inc, number 34780, Jan.
• Lauren Cohen & Yiwen Lu & Quoc H. Nguyen, 2026, "Mimicking Finance," NBER Working Papers, National Bureau of Economic Research, Inc, number 34849, Feb.
• Yijie Wang & Hao Gao & Campbell R. Harvey & Yan Liu & Xinyuan Tao, 2026, "Machine Learning Meets Markowitz," NBER Working Papers, National Bureau of Economic Research, Inc, number 34861, Feb.
• Anna Denkowska & Krystian Szczȩsny & Stanisław Wanat, 2026, "Nonlinear dependencies in Solvency II: risk aggregation with deep neural networks," Risk Management, Palgrave Macmillan, volume 28, issue 2, pages 1-31, May, DOI: 10.1057/s41283-026-00191-1.
• Andre Mouton, 2026, "Measuring Task-Level Technological Exposure: A Language Model Approach," Working Papers, Wake Forest University, Economics Department, number 132, Feb.
• Elliot Beck & Franziska Eckert & Linus Kühne & Helge Liebert & Rina Rosenblatt-Wisch, 2026, "Measuring economic outlook in the news," Working Papers, Swiss National Bank, number 2026-04.
• Cosimo Magazzino & Benedetta Coluccia & Donatella Porrini & Tulia Gattone, 2026, "Agents of digitalization: gendered employment patterns and broadband access across Asian economies," The Annals of Regional Science, Springer;Western Regional Science Association, volume 75, issue 1, pages 1-28, March, DOI: 10.1007/s00168-025-01432-z.
• Vaibhav Gagneja & Mayank Gupta & Sanjay Batish & Poonam Saini & Sudesh Rani, 2026, "ES-LSTM: a hybrid model for accurate time series forecasting in financial markets," Digital Finance, Springer, volume 8, issue 1, pages 1-21, March, DOI: 10.1007/s42521-025-00173-0.
• Jinwon Kim & Seongsoo Jang & Ulrike Gretzel & Chulmo Koo, 2026, "Special issue on “Smart tourism 2.0: Perspectives with geospatial data and AI”," Electronic Markets, Springer;IIM University of St. Gallen, volume 36, issue 1, pages 1-5, December, DOI: 10.1007/s12525-025-00866-9.
• Radmir Mishelevich Leushuis & Nicolai Petkov, 2026, "Advances in forecasting realized volatility: a review of methodologies," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 12, issue 1, pages 1-29, December, DOI: 10.1186/s40854-025-00809-5.
• Hugo Gobato Souto & Amir Moradi, 2026, "Enhancing financial risk management: a novel multivariate neural network approach for realized covariance matrix prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 12, issue 1, pages 1-26, December, DOI: 10.1186/s40854-025-00816-6.
• Gideon Mazuruse & Brighton Nyagadza, 2026, "Barriers to adopting climate change awareness and education in Zimbabwe: a hybrid structural equation modelling and extreme gradient boosting approach," Quality & Quantity: International Journal of Methodology, Springer, volume 60, issue 1, pages 2883-2912, February, DOI: 10.1007/s11135-025-02384-4.
• Alexa Kaminski & Alistair Macaulay & Wenting Song, 2026, "Monetary Policy Narratives and the Transmission of Monetary Policy," School of Economics Discussion Papers, School of Economics, University of Surrey, number 0126, Jan.
• Nicholas Lacoste & Zehra Farooq, 2026, "Optimal Audit Targeting with Machine Learning: Evidence from Pakistan," Working Papers, Tulane University, Department of Economics, number 2603, Feb.
• Yachou Najlae & Abahman Omar & Hakimi Khalid, 2026, "Designing an LSTM-Based Model for Financial Asset Forecasting Using Machine Learning," Central European Economic Journal, Sciendo, volume 13, issue 60, pages 1-23, DOI: 10.2478/ceej-2026-0001.

2025

• Angel Anchev & Borislav Stoyanov & Milka Atanasova & Yaroslav Argirov & Boris Petkov, 2025, "Improving in microhardness of C45 steel obtained via electron beam hardening using a one-factor-at-a-time technique," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, volume 8, issue 5, pages 1255-1270.
• Habib ZOUAOUI & Meryem-Nadjat NAAS, 2025, "Portfolio Optimization Based on MPT-LSTM Neural Networks: A case study of Cryptocurrency Markets," Finance, Accounting and Business Analysis, University of National and World Economy, Institute for Economics and Politics, volume 7, issue 1, pages 82-98, June.
• Adedeji Gbadebo, 2025, "Stock Price Forecasting Using a Time-Series Long Short-Term Memory Model," Finance, Accounting and Business Analysis, University of National and World Economy, Institute for Economics and Politics, volume 7, issue 2, pages 304-322, December.
• Alejandro Adame-Castaneda & Ivan Alejandro Salas-Durazo, 2025, "Politicas publicas para el desarrollo rural integral en Mexico una aproximacion multidimensional al ODS 2 Hambre Cero," Scientia et PRAXIS, AMIDI Editorial, volume 5, issue 10, pages 34-63.
• Ahmet Akusta, 2025, "Predicting Market Sensitivity: The Role of Board Structure in the Beta Coefficient of Software Companies on the Nasdaq Global Select Market," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., volume 40, issue 123, pages 14-34, April, DOI: https://doi.org/10.33203/mfy.159699.
• Johannes Haushofer & Paul Niehaus & Carlos Paramo & Edward Miguel & Michael Walker, 2025, "Targeting Impact versus Deprivation," American Economic Review, American Economic Association, volume 115, issue 6, pages 1936-1974, June, DOI: 10.1257/aer.20221650.
• Marlène Koffi, 2025, "Innovative Ideas and Gender (In)equality," American Economic Review, American Economic Association, volume 115, issue 7, pages 2207-2236, July, DOI: 10.1257/aer.20211811.
• Lorenzo Magnolfi & Jonathon McClure & Alan Sorensen, 2025, "Triplet Embeddings for Demand Estimation," American Economic Journal: Microeconomics, American Economic Association, volume 17, issue 1, pages 282-307, February, DOI: 10.1257/mic.20220248.
• Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2025, "A Machine Learning Approach to Analyze and Support Anticorruption Policy," American Economic Journal: Economic Policy, American Economic Association, volume 17, issue 2, pages 162-193, May, DOI: 10.1257/pol.20210618.
• Sendhil Mullainathan, 2025, "Economics in the Age of Algorithms," AEA Papers and Proceedings, American Economic Association, volume 115, pages 1-23, May, DOI: 10.1257/pandp.20251118.
• Abi Adams & Mathias Fjællegaard Jensen & Barbara Petrongolo, 2025, "The Contribution of Employee-Led and Employer-Led Work Flexibility to the Motherhood Wage Gap," AEA Papers and Proceedings, American Economic Association, volume 115, pages 243-247, May, DOI: 10.1257/pandp.20251015.
• Lefteris Andreadis & Eleni Kalotychou & Manolis Chatzikonstantinou & Christodoulos Louca & Christos A. Makridis, 2025, "Local Heterogeneity in Artificial Intelligence Jobs over Time and Space," AEA Papers and Proceedings, American Economic Association, volume 115, pages 29-34, May, DOI: 10.1257/pandp.20251001.
• Tania Babina & Anastassia Fedyk & Alex He & James Hodson, 2025, "Artificial Intelligence Makes Firm Operating Performance Less Volatile," AEA Papers and Proceedings, American Economic Association, volume 115, pages 35-39, May, DOI: 10.1257/pandp.20251002.
• Avi Goldfarb & Xianda (Henry) He & Florenta Teodoridis, 2025, "Patterns of Artificial Intelligence Adoption by Hospitals," AEA Papers and Proceedings, American Economic Association, volume 115, pages 40-45, May, DOI: 10.1257/pandp.20251003.
• Stefania Albanesi & António Dias da Silva & Juan F. Jimeno & Ana Lamo & Alena Wabitsch, 2025, "AI and Women's Employment in Europe," AEA Papers and Proceedings, American Economic Association, volume 115, pages 46-50, May, DOI: 10.1257/pandp.20251044.
• Benjamin Labaschin & Tyna Eloundou & Sam Manning & Pamela Mishkin & Daniel Rock, 2025, "Extending "GPTs Are GPTs" to Firms," AEA Papers and Proceedings, American Economic Association, volume 115, pages 51-55, May, DOI: 10.1257/pandp.20251045.
• Mauro Cazzaniga & Augustus Panton & Longji Li & Carlo Pizzinelli & Marina M. Tavares, 2025, "A Gender Lens on Labor Market Exposure to AI," AEA Papers and Proceedings, American Economic Association, volume 115, pages 56-61, May, DOI: 10.1257/pandp.20251046.
• Philippe Aghion & Simon Bunel & Xavier Jaravel & Thomas Mikaelsen & Alexandra Roulet & Jakob Søgaard, 2025, "How Different Uses of AI Shape Labor Demand: Evidence from France," AEA Papers and Proceedings, American Economic Association, volume 115, pages 62-67, May, DOI: 10.1257/pandp.20251047.
• Abe Dunn & Eric English & Kyle Hood & Lowell Mason & Brian Quistorff, 2025, "Economic Measurement Lost in a Random Forest? A Case Study of Employment Data," AEA Papers and Proceedings, American Economic Association, volume 115, pages 68-72, May, DOI: 10.1257/pandp.20251103.
• Tatjana Dahlhaus & Reinhard Ellwanger & Gabriela Galassi & Pierre-Yves Yanni, 2025, "From Online Job Postings to Economic Insights: A Machine Learning Approach to Structuring Naturally Occurring Data," AEA Papers and Proceedings, American Economic Association, volume 115, pages 73-78, May, DOI: 10.1257/pandp.20251104.
• Gary Cornwall & Marina Gindelsky, 2025, "Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach," AEA Papers and Proceedings, American Economic Association, volume 115, pages 79-84, May, DOI: 10.1257/pandp.20251105.
• Andrew Caplin, 2025, "Data Engineering for Cognitive Economics," Journal of Economic Literature, American Economic Association, volume 63, issue 1, pages 164-196, March, DOI: 10.1257/jel.20241351.
• Melissa Dell, 2025, "Deep Learning for Economists," Journal of Economic Literature, American Economic Association, volume 63, issue 1, pages 5-58, March, DOI: 10.1257/jel.20241733.
• George Loewenstein & Zachary Wojtowicz, 2025, "The Economics of Attention," Journal of Economic Literature, American Economic Association, volume 63, issue 3, pages 1038-1089, September, DOI: 10.1257/jel.20241665.
• Quintana Pablo & Herrera-Gomez Marcos, 2025, "Redefining Regions in Space and Time: A Deep Learning Method for Spatio-Temporal Clustering," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4831, Dec.
• Kutlu ERGÃœN, 2025, "From Man to Man with AI Navigation: An Essay on the Homo Economicus Strengthened and Weakened by AI," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, volume 1, issue 3, pages 31-42, September.
• Katleho Makatjane & Claris Shoko, 2025, "Explainable Deep Learning for Financial Risk: Joint VaR and ES Forecasting Using ESRNN in the Bitcoin Market," The African Finance Journal, Africagrowth Institute, volume 27, issue 1, pages 53-69.
• Jieyu Chen & Sebastian Lerch & Melanie Schienle & Tomasz Serafin & Rafal Weron, 2025, "Probabilistic intraday electricity price forecasting using generative machine learning," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/25/05.
• Arkadiusz Lipiecki & Kaja Bilinska & Nikolaos Kourentzes & Rafal Weron, 2025, "Stealing accuracy: Predicting day-ahead electricity prices with Temporal Hierarchy Forecasting (THieF)," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/25/06.
• Ayşegül PEKER & Duygu TUNALI, 2025, "The Comparison of Artificial Neural Networks and Panel Data Analysis on Profitability Prediction: The Case of Real Estate Investment Trusts," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 10, issue 1, pages 160-183, DOI: https://doi.org/10.30784/epfad.1602.
• Yunus Emre Akdoğan, 2025, "The Role of Financial Indicators in the Prediction of Voluntary Carbon Disclosure: A Comparative Analysis with Machine Learning Methods," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 10, issue 3, pages 949-970, DOI: 10.30784/epfad.1651693.
• Çiğdem Yerli, 2025, "Evaluating the Impact of ESG and Decarbonization Metrics on Stock Price Prediction," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 10, issue SI, pages 252-274, DOI: 10.30784/epfad.1669184.
• Yunus Emre Gür & Ahmet İhsan Şimşek & Emre Bulut, 2025, "Artificial Intelligence-Assisted Machine Learning Methods For Forecasting Green Bond Index: A Comparative Analysis," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 9, issue 4, pages 628-655, DOI: https://doi.org/10.30784/epfad.1495.
• Lev A. Bulanov & Alexei V. Kalina & Vadim V. Krivorotov, 2025, "Clustering of Russian Manufacturing Companies by Indicators of Their Financial Condition Using Machine Learning Technologies," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, volume 24, issue 2, pages 584-621, DOI: https://doi.org/10.15826/vestnik.20.
• Lev A. Bulanov & Alexei V. Kalina & Vadim V. Krivorotov, 2025, "Selection of Informative Indicators for Assessing the Economic Security of Russian Companies," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, volume 24, issue 4, pages 1371-1415, DOI: https://doi.org/10.15826/vestnik.20.
• Pablo Quintana & Marcos Herrera-Gómez, 2025, "Redefining Regions in Space and Time: A Deep Learning Method for Spatio-Temporal Clustering," Working Papers, Red Nacional de Investigadores en Economía (RedNIE), number 368, Aug.
• Jonathan Garita-Garita & César Ulate-Sancho, 2025, "Forecasting Nominal Exchange Rate using Deep Neural Networks," Documentos de Trabajo, Banco Central de Costa Rica, number 2505, Jul.
• Kevin Ungar & Camelia Oprean-Stan, 2025, "Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.'s Net Income and Stock Prices," Papers, arXiv.org, number 2501.06587, Jan.
• Arkadiusz Lipiecki & Kaja Bilinska & Nicolaos Kourentzes & Rafal Weron, 2025, "Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)," Papers, arXiv.org, number 2508.11372, Aug.
• Elliot Beck & Franziska Eckert & Linus Kuhne & Helge Liebert & Rina Rosenblatt-Wisch, 2025, "Measuring economic outlook in the news," Papers, arXiv.org, number 2511.04299, Nov, revised Feb 2026.
• Rubén Fernández-Fuertes, 2025, "Monetary Policy Shocks: A New Hope. Large Language Models and Central Bank Communication," BAFFI CAREFIN Working Papers, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy, number 25257.
• Kamelia Ahmadkhan & Abdolreza Yazdani-Chamzini & Alireza Bakhshizadeh & Jonas Šaparauskas & Zenonas Turskis & Niousha Zeidyahyaee, 2025, "Promoting reverse logistics decisions using a new hybrid model based on deep learning and failure mode and effects analysis approaches," E&M Economics and Management, Technical University of Liberec, Faculty of Economics, volume 28, issue 4, pages 79-98, December, DOI: 10.15240/tul/001/2025-4-006.
• Julien Pascal, 2025, "Solving economic models with neural networks without backpropagation," BCL working papers, Central Bank of Luxembourg, number 196, Apr.
• Firdevs Nur UYKUN & Busra Zeynep TEMOCIN, 2025, "A Machine Learning Integrated Portfolio Rebalance Framework with Risk Aversion Adjustment," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 19, issue 2, pages 173-197.
• Pilar García & Diego Torres, 2025, "Perceiving central bank communications through press coverage," Working Papers, Banco de España, number 2505, Jan, DOI: https://doi.org/10.53479/38922.
• Canio Benedetto & Sara Crestini & Alessandro de Gregorio & Marco de Leonardis & Andrea del Monaco & Daniele Gulino & Paolo Massaro & Francesca Monacelli & Lorenzo Rubeo, 2025, "Applying artificial intelligence to support regulatory reporting management: the experience at Banca d'Italia," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 927, Apr.
• Daniele Licari & Canio Benedetto & Daniele Bovi & Praveen Bushipaka & Alessandro De Gregorio & Marco De Leonardis & Tommaso Cucinotta, 2025, "A novel multi-step-prompt approach for LLM-based Q&As on banking supervisory regulations," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 935, Apr.
• Milovan Rankov, 2025, "Komparativna Analiza Modela Kreditnog Skoringa: Konvencijalni Vs Modeli Bazirani Na Mašinskom I Dubokom Učenju," Ekonomske ideje i praksa, Faculty of Economics and Business, University of Belgrade, issue 57, pages 29-45, June.
• Luis Menéndez & Daniel Montolio & Hannes Mueller & Francesco Slataper, 2025, "Breaking the Echo Chamber: Social Media Networks and Political Conflict," Working Papers, Barcelona School of Economics, number 1505, Sep.
• Katia Boria & Andrea Luciani & Sabina Marchetti & Marco Viticoli, 2025, "Siamese neural networks for detecting banknote printing defects," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Data science in central banking: enhancing the access to and sharing of data".
• Hanno Kase & Leonardo Melosi & Matthias Rottner, 2025, "Estimating nonlinear heterogeneous agent models with neural networks," BIS Working Papers, Bank for International Settlements, number 1241, Jan.
• Hanno Kase & Matthias Rottner & Fabio Stohler, 2025, "Generative economic modeling," BIS Working Papers, Bank for International Settlements, number 1312, Dec.
• Elizaveta Volgina, 2025, "Forecasting Inflation Using News Indices," Russian Journal of Money and Finance, Bank of Russia, volume 84, issue 1, pages 26-59, March.
• Anastasia Matevosova, 2025, "Modelling Trust in the Central Bank Using Sentiment Analysis," Russian Journal of Money and Finance, Bank of Russia, volume 84, issue 1, pages 3-25, March.
• Oleg Kryzhanovskiy & Anastasia Mogilat & Zhanna Shuvalova & Dmitry Gvozdev, 2025, "Using LSTM Neural Networks for Nowcasting and Forecasting GVA of Industrial Sectors," Russian Journal of Money and Finance, Bank of Russia, volume 84, issue 1, pages 93-104, March.
• Marcus Buckmann & Ed Hill, 2025, "Improving text classification: logistic regression makes small LLMs strong and explainable ‘tens-of-shot’ classifiers," Bank of England working papers, Bank of England, number 1127, May.
• Mattera Raffaele, 2025, "Forecasting High-Dimensional Portfolios," Journal of Time Series Econometrics, De Gruyter, volume 17, issue 1, pages 35-67, DOI: 10.1515/jtse-2023-0011.
• Baronchelli Adelaide & Ricciuti Roberto, 2025, "The Battlefield and the Wire: Linking Cyber and Material Conflicts, 2000–2014," Peace Economics, Peace Science, and Public Policy, De Gruyter, volume 31, issue 3, pages 365-380, DOI: 10.1515/peps-2025-0050.
• Goulet Coulombe Philippe, 2025, "To Bag is to Prune," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 29, issue 6, pages 669-697, DOI: 10.1515/snde-2023-0030.
• Clément Gorin & Stephan Heblich & Yanos Zylberberg, 2025, "State of the Art: Economic Development Through the Lens of Paintings," Bristol Economics Discussion Papers, School of Economics, University of Bristol, UK, number 25/793, Apr.
• Bachmair, K. & Schmitz, N., 2025, "Forecasting Macro with Finance," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2574, Nov.
• Benjamin Born & Nora Lamersdorf & Jana-Lynn Schuster & Sascha Steffen, 2025, "From Tweets to Transactions: High-Frequency Inflation Expectations, Consumption, and Stock Returns," CESifo Working Paper Series, CESifo, number 12361.
• Lucien Chaffa & Martin Trépanier & Thierry Warin, 2025, "Beyond PPML: Exploring Machine Learning Alternatives for Gravity Model Estimation in International Trade," CIRANO Working Papers, CIRANO, number 2025s-14, May.
• Jesús Villota, 2025, "Predicting Market Reactions to News: An LLM-Based Approach Using Spanish Business Articles," Working Papers, CEMFI, number wp2025_2501, Jan.
• Juan Sebastian Vallejo Triana, 2025, "Las ilusiones de la democracia: el voto program√°tico en Colombia," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 21319, Feb.
• Alvaro Riascos Villegas, 2025, "El Potencial Impacto del Aprendizaje de M√°quinas en el Dise√±o de las Pol√≠ticas P√∫blicas en Colombia: Una d√©cada de experiencias," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 21340, Feb.
• Juan Jos√© Rinc√≥n Brice√±o, 2025, "Colombian economic activity nowcasting: addressing nonlinearities and high dimensionality through machine-learning," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 21388, Jun.
• Carlos Castro-Iragorri & Manuel Parra-Diaz, 2025, "Stability focused end to end frameworks for risk budgeting portfolios," Documentos de Trabajo, Universidad del Rosario, number 21367, Mar.
• Adriana María Flórez Laiseca & Elkin Argiro Muñoz Arroyave, 2025, "Business resilience in the Quindío agro-industrial cluster: a forward-looking approach based on business networks," Revista Tendencias, Universidad de Narino, volume 26, issue 02, pages 217-240, July, DOI: 10.22267/rtend.2526.
• Guo, Hongfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2025, "Learning Volatility:A Bayesian Neural Stochastic Framework," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 47944, Sep.
• Christopher Mwololo Fred, 2025, "Comparative Analysis of Machine Learning Algorithms for Enhancing Social Media Marketing and Decision-Making in Kenyan SMEs," African Journal of Commercial Studies, African Journal of Commercial Studies, volume 6, issue 1, DOI: 10.59413/ajocs/v6.i.1.4.
• Daniel Graeber & Lorenz Meister & Carsten Schröder & Sabine Zinn, 2025, "Random Forests for Labor Market Analysis: Balancing Precision and Interpretability," SOEPpapers on Multidisciplinary Panel Data Research, DIW Berlin, The German Socio-Economic Panel (SOEP), number 1230.
• Echevin, Damien & Fotso, Guy & Bouroubi, Yacine & Coulombe, Harold & Li, Qing, 2025, "Combining survey and census data for improved poverty prediction using semi-supervised deep learning," Journal of Development Economics, Elsevier, volume 172, issue C, DOI: 10.1016/j.jdeveco.2024.103385.
• Ma, Dan & Zhu, Yanjin & Lee, Chien-Chiang, 2025, "The impact of new energy pilot city policies on urban green innovation: Evidence from China’s city level," Economic Analysis and Policy, Elsevier, volume 87, issue C, pages 585-604, DOI: 10.1016/j.eap.2025.06.029.
• Peng, Michael & Stern, Elisheva R. & Hu, Hanwen, 2025, "Forecasting China bond default with severe class-imbalanced data: A simple learning model with causal inference," Economic Modelling, Elsevier, volume 144, issue C, DOI: 10.1016/j.econmod.2024.106985.
• Limosani, Michele & Millemaci, Emanuele & Mustica, Paolo, 2025, "Do green policies enhance short-term economic growth? Assessing EU Recovery and Resilience Plans through the lens of Sustainable Development Goals," Economic Modelling, Elsevier, volume 147, issue C, DOI: 10.1016/j.econmod.2025.107044.
• Zhang, Heng-Guo & Wang, Shihong & Xie, Yuchi, 2025, "How does news-driven monetary policy frictions affect nonperforming loans?--Taking Chinese commercial banks as an example," The North American Journal of Economics and Finance, Elsevier, volume 76, issue C, DOI: 10.1016/j.najef.2024.102353.
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• Haghighi, Maryam & Joseph, Andreas & Kapetanios, George & Kurz, Christopher & Lenza, Michele & Marcucci, Juri, 2025, "Machine Learning for Economic Policy," Journal of Econometrics, Elsevier, volume 249, issue PC, DOI: 10.1016/j.jeconom.2025.105970.
• Caner, Mehmet & Daniele, Maurizio, 2025, "Deep learning based residuals in non-linear factor models: Precision matrix estimation of returns with low signal-to-noise ratio," Journal of Econometrics, Elsevier, volume 251, issue C, DOI: 10.1016/j.jeconom.2025.106083.
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• Tsionas, Mike & Zelenyuk, Valentin & Zhang, Xibin, 2025, "Goodness-of-fit in production models: A Bayesian perspective," European Journal of Operational Research, Elsevier, volume 324, issue 2, pages 644-653, DOI: 10.1016/j.ejor.2025.01.030.
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• Albani, V.V.L. & Marcavillaca, R.T. & Moreira, P.S.E. & Avila, S.L. & Geremia, M. & Piovezan, R.P.B. & Sica, E.T. & Santos, E., 2025, "Short-term forecasting of forward prices in the Brazilian electricity market with a hybrid stochastic-neural network model," Energy Economics, Elsevier, volume 148, issue C, DOI: 10.1016/j.eneco.2025.108651.
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• Dragicevic, Arnaud Z. & Thongtai, Chanon & Pecora, Nicolò, 2025, "Assessing the potential for biofuel production within a conventional fuel system," Energy Economics, Elsevier, volume 152, issue C, DOI: 10.1016/j.eneco.2025.108991.
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• Sina, A. & Billio, M. & Dufour, A. & Rocciolo, F. & Varotto, S., 2025, "The systemic risk of leveraged and covenant-lite loan syndications," International Review of Financial Analysis, Elsevier, volume 97, issue C, DOI: 10.1016/j.irfa.2024.103738.
• Hu, Nan & Yin, Xuebao & Yao, Yuhang, 2025, "A novel HAR-type realized volatility forecasting model using graph neural network," International Review of Financial Analysis, Elsevier, volume 98, issue C, DOI: 10.1016/j.irfa.2024.103881.
• François, Pascal & Gauthier, Geneviève & Godin, Frédéric & Mendoza, Carlos Octavio Pérez, 2025, "Is the difference between deep hedging and delta hedging a statistical arbitrage?," Finance Research Letters, Elsevier, volume 73, issue C, DOI: 10.1016/j.frl.2024.106590.
• Hu, Wendi & Shao, Chujian & Zhang, Wenyu, 2025, "Predicting U.S. bank failures and stress testing with machine learning algorithms," Finance Research Letters, Elsevier, volume 75, issue C, DOI: 10.1016/j.frl.2025.106802.
• Doğan, Murat & Sayılır, Özlem & Komath, Muhammed Aslam Chelery & Çimen, Emre, 2025, "Prediction of market value of firms with corporate sustainability performance data using machine learning models," Finance Research Letters, Elsevier, volume 77, issue C, DOI: 10.1016/j.frl.2025.107085.
• Lu, Zhichao & Xu, Yuhong & Zhang, Yue & Zhao, Xinyao, 2025, "Is it difficult to predict the price movements of high-volatility assets," Finance Research Letters, Elsevier, volume 85, issue PB, DOI: 10.1016/j.frl.2025.107980.
• Liu, Ying & Liu, Shuang & Lu, Yu, 2025, "Supply chain financial risk assessment: A modified graph attention neural network," Finance Research Letters, Elsevier, volume 86, issue PA, DOI: 10.1016/j.frl.2025.108285.
• Chon, Sora & Kim, Jaehoon & Kim, Jaeho, 2025, "Multifaceted variability in LLM-driven stock recommendations," Finance Research Letters, Elsevier, volume 86, issue PG, DOI: 10.1016/j.frl.2025.108923.
• Lütkebohmert, Eva & Sester, Julian & Shen, Hongyi, 2025, "Name concentration risk in Multilateral Development Banks’ portfolios: Measurement and capital adequacy implications," Global Finance Journal, Elsevier, volume 67, issue C, DOI: 10.1016/j.gfj.2025.101154.
• Baumgärtner, Martin & Zahner, Johannes, 2025, "Whatever it takes to understand a central banker — Embedding their words using neural networks," Journal of International Economics, Elsevier, volume 157, issue C, DOI: 10.1016/j.jinteco.2025.104101.
• Padha, Vimarsh & Chaubal, Aditi, 2025, "Multiscale foreign exchange dynamics in India: A wavelet approach," International Economics, Elsevier, volume 184, issue C, DOI: 10.1016/j.inteco.2025.100652.
• Li, Shuyue & Yarovaya, Larisa & Mishra, Tapas, 2025, "Machine learning, memory and efficiency in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, volume 105, issue C, DOI: 10.1016/j.intfin.2025.102210.
• Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2025, "Predicting IPO first-day returns: Evidence from machine learning analyses," Journal of Banking & Finance, Elsevier, volume 178, issue C, DOI: 10.1016/j.jbankfin.2025.107500.
• Nutarelli, Federico & Edet, Samuel & Gnecco, Giorgio & Riccaboni, Massimo, 2025, "Predicting the technological complexity of global cities based on unsupervised and supervised machine learning methods," Journal of Economic Behavior & Organization, Elsevier, volume 234, issue C, DOI: 10.1016/j.jebo.2025.107011.
• Carow, Johannes & Witzig, Niklas M., 2025, "Time pressure and strategic risk-taking in professional chess," Journal of Economic Behavior & Organization, Elsevier, volume 238, issue C, DOI: 10.1016/j.jebo.2025.107218.
• Liu, Mengxiao & Wang, Luhang & Yi, Yimin, 2025, "Quality innovation, cost innovation, exporting, and firm productivity evolution: Evidence from the Chinese electronics industry," Journal of Economic Behavior & Organization, Elsevier, volume 239, issue C, DOI: 10.1016/j.jebo.2025.107232.
• Mertzanis, Charilaos & Kampouris, Ilias & Samitas, Aristeidis, 2025, "Climate change and U.S. Corporate bond market activity: A machine learning approach," Journal of International Money and Finance, Elsevier, volume 151, issue C, DOI: 10.1016/j.jimonfin.2024.103259.
• Ashwin, Julian & Beaudry, Paul & Ellison, Martin, 2025, "Neural network learning for nonlinear economies," Journal of Monetary Economics, Elsevier, volume 149, issue C, DOI: 10.1016/j.jmoneco.2024.103723.
• Kampouris, Ilias & Mertzanis, Charilaos & Samitas, Aristeidis, 2025, "Natural disaster shocks and commodity market volatility: A machine learning approach," Pacific-Basin Finance Journal, Elsevier, volume 90, issue C, DOI: 10.1016/j.pacfin.2024.102618.
• Chiu, I-Chan & Hung, Mao-Wei, 2025, "Finance-specific large language models: Advancing sentiment analysis and return prediction with LLaMA 2," Pacific-Basin Finance Journal, Elsevier, volume 90, issue C, DOI: 10.1016/j.pacfin.2024.102632.
• Cheng, Zijian & Li, Tianze & Liu, Zhangxin (Frank), 2025, "Unveiling the veil: Identifying potential shell firms using machine learning approaches," Pacific-Basin Finance Journal, Elsevier, volume 92, issue C, DOI: 10.1016/j.pacfin.2025.102798.
• Li, Xiao-Xin & Xie, Chi & Wang, Gang-Jin & Zhu, You & Li, Zhao-Chen & Zhang, Zhi-Yu, 2025, "Enhancing stock market return predictability by using a novel autoencoder-based aggregate EPU index," Pacific-Basin Finance Journal, Elsevier, volume 93, issue C, DOI: 10.1016/j.pacfin.2025.102873.
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• Kumar, Satish & Rao, Amar & Dhochak, Monika, 2025, "Hybrid ML models for volatility prediction in financial risk management," International Review of Economics & Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.iref.2025.103915.
• Jiang, Yifu & Olmo, Jose & Atwi, Majed, 2025, "High-dimensional multi-period portfolio allocation using deep reinforcement learning," International Review of Economics & Finance, Elsevier, volume 98, issue C, DOI: 10.1016/j.iref.2025.103996.
• Thomas Persson, 2025, "Machine Learning Methods," Journal of Economics and Econometrics, Economics and Econometrics Society, volume 68, issue 2, pages 106-129.
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• Koukorinis, Andreas & Peters, Gareth W. & Germano, Guido, 2025, "Generative-discriminative machine learning models for high-frequency financial regime classification," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 128016, Jun.
• Martín, Roberto Spacey & Ranger, Nicola & Schimanski, Tobias & Leippold, Markus, 2025, "Empirically assessing corporate adaptation and resilience disclosure using AI," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 130809, Sep.
• Artur Kulpa & Grzegorz Wojarnik, 2025, "Prompt Engineering in Finance: An LLM-Based Multi-Agent Architecture for Decision Support," European Research Studies Journal, European Research Studies Journal, volume 0, issue 3, pages 1201-1217.
• Viktor Ivanovich Blanutsa, 2025, "Creating the First Autonomous Systems of Internet in Siberia as a Spatial Diffusion of Innovations," Spatial Economics=Prostranstvennaya Ekonomika, Economic Research Institute, Far Eastern Branch, Russian Academy of Sciences (Khabarovsk, Russia), issue 1, pages 7-32, DOI: https://dx.doi.org/10.14530/se.2025.
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• Monica Bonacina & Romolo Consigna Tokong, 2025, "Is Italy on Track? A Data-Driven Forecast for Road Transport Decarbonisation by 2030," Working Papers, Fondazione Eni Enrico Mattei, number 2025.19, Sep.
• Leland D. Crane & Xiaoyu Ge & Flora Haberkorn & Rithika Iyengar & Seung Jung Lee & Viviana Luccioli & Ryan Panley & Nitish R. Sinha, 2025, "LLM on a Budget: Active Knowledge Distillation for Efficient Classification of Large Text Corpora," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2025-108, Dec, DOI: 10.17016/FEDS.2025.108.
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• Carlo Drago & Massimo Arnone & Angelo Leogrande, 2025, "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," Working Papers, HAL, number hal-04994903, Mar.
• Angelo Leogrande & Nicola Magaletti & Valeria Notarnicola & Mauro Di Molfetta & Stefano Mariani, 2025, "Data-Driven Welding Quality Assessment: Leveraging IoT and Machine Learning in Industrial Practice," Working Papers, HAL, number hal-05043506, Apr.
• Margareth Antonicelli & Carlo Drago & Alberto Costantiello & Angelo Leogrande, 2025, "Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms," Working Papers, HAL, number hal-05091404, May.
• Carlo Drago & Alberto Costantiello & Marco Savorgnan & Angelo Leogrande, 2025, "Driving AI Adoption in the EU: A Quantitative Analysis of Macroeconomic Influences," Working Papers, HAL, number hal-05102974, Jun.
• Onofrio Resta & Emanuela Resta & Alberto Costantiello & Piergiuseppe Liuzzi & Angelo Leogrande, 2025, "Environmental Complexity and Respiratory Health: A Data-Driven Exploration Across European Regions," Working Papers, HAL, number hal-05243548, Sep.
• Koji Takahashi & Joon Suk Park, 2025, "Generative AI for Surveys on Payment Apps: AIs' View on Privacy and Technology," IMES Discussion Paper Series, Institute for Monetary and Economic Studies, Bank of Japan, number 25-E-13, Sep.
• Bhaskar Tripathi & Rakesh Kumar Sharma, 2025, "Cryptocurrency Exchanges and Traditional Markets: A Multi-algorithm Liquidity Comparison Using Multi-criteria Decision Analysis," Computational Economics, Springer;Society for Computational Economics, volume 65, issue 5, pages 2649-2677, May, DOI: 10.1007/s10614-024-10655-9.
• Edson Pindza & Jules Clement & Sutene Mwambi & Nneka Umeorah, 2025, "Neural Network for Valuing Bitcoin Options Under Jump-Diffusion and Market Sentiment Model," Computational Economics, Springer;Society for Computational Economics, volume 66, issue 3, pages 2305-2342, September, DOI: 10.1007/s10614-024-10792-1.
• Zareh Asatryan & Carlo Birkholz & Friedrich Heinemann, 2025, "Evidence-based policy or beauty contest? An LLM-based meta-analysis of EU cohesion policy evaluations," International Tax and Public Finance, Springer;International Institute of Public Finance, volume 32, issue 2, pages 625-655, April, DOI: 10.1007/s10797-024-09875-4.
• José Alberto Molina & David Iñiguez & Gonzalo Ruiz & Alfonso Tarancón, 2025, "Networks in Household and Population Socifal Sciences: Encouraging the Need for More Collaborations," Journal of Family and Economic Issues, Springer, volume 46, issue 4, pages 1041-1053, December, DOI: 10.1007/s10834-025-10065-5.
• Hui Dong & Xiao Pan & Xiao Chen & Jing Sun & Shuhai Wang, 2025, "DyAdapTransformer: dynamic adaptive spatial–temporal graph transformer for traffic flow prediction," Journal of Geographical Systems, Springer, volume 27, issue 2, pages 229-255, April, DOI: 10.1007/s10109-025-00464-5.
• Nicholas A. Pairolero & Alexander V. Giczy & Gerard Torres & Tisa Islam Erana & Mark A. Finlayson & Andrew A. Toole, 2025, "The artificial intelligence patent dataset (AIPD) 2023 update," The Journal of Technology Transfer, Springer, volume 50, issue 6, pages 2587-2610, December, DOI: 10.1007/s10961-025-10189-8.
• Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025, "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, volume 65, issue 1, pages 107-148, July, DOI: 10.1007/s11156-023-01192-x.
• Adelaide Baronchelli & Roberto Ricciuti & Mattia Viale, 2025, "Elite persistence in medieval Venice after the Black Death," Rivista di storia economica, Società editrice il Mulino, issue 3, pages 307-329.
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• Clément Gorin & Stephan Heblich & Yanos Zylberberg, 2025, "State of the Art: Economic Development Through the Lens of Paintings," NBER Working Papers, National Bureau of Economic Research, Inc, number 33976, Jun.
• Jesús Fernández-Villaverde, 2025, "Deep Learning for Solving Economic Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 34250, Sep.
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• Antonicelli, Margareth & Drago, Carlo & Costantiello, Alberto & Leogrande, Angelo, 2025, "Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms," OSF Preprints, Center for Open Science, number tk87m_v1, Jun, DOI: 10.31219/osf.io/tk87m_v1.
• Drago, Carlo & Arnone, Massimo & Leogrande, Angelo, 2025, "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," SocArXiv, Center for Open Science, number 4r8ux_v1, Mar, DOI: 10.31219/osf.io/4r8ux_v1.
• Paolo Giordani, 2025, "SMARTboost Learning for Tabular Data," Journal of Financial Econometrics, Oxford University Press, volume 23, issue 3, pages 929-985.
• Manfred Herdt & Hermann Schulte-Mattler, 2025, "Operationalization of the construct “Business model of a Bank”: clustering analyses with deep neural networks," Journal of Banking Regulation, Palgrave Macmillan, volume 26, issue 3, pages 392-407, September, DOI: 10.1057/s41261-025-00269-y.
• Daniel Boller & Michael Lechner & Gabriel Okasa, 2025, "The effect of sport in online dating: evidence from causal machine learning," Humanities and Social Sciences Communications, Palgrave Macmillan, volume 12, issue 1, pages 1-13, December, DOI: 10.1057/s41599-025-04566-9.
• Jesus Fernandez-Villaverde, 2025, "Deep Learning for Solving Economic Models," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 25-017, Sep.
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• Drago, Carlo & Arnone, Massimo & Leogrande, Angelo, 2025, "Exploring N₂O Emissions at World Level: Advanced Econometric and Machine Learning Approaches in the ESG Context," MPRA Paper, University Library of Munich, Germany, number 124006, Mar.
• Magaletti, Nicola & Notarnicola, Valeria & Di Molfetta, Mauro & Mariani, Stefano & Leogrande, Angelo, 2025, "Data-Driven Welding Quality Assessment: Leveraging IoT and Machine Learning in Industrial Practice," MPRA Paper, University Library of Munich, Germany, number 124548, Apr.
• Antonicelli, Margareth & Drago, Carlo & Costantiello, Alberto & Leogrande, Angelo, 2025, "Analyzing Income Inequalities across Italian regions: Instrumental Variable Panel Data, K-Means Clustering and Machine Learning Algorithms," MPRA Paper, University Library of Munich, Germany, number 124910, May.