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The Impact of Machine Learning on Economics

In: The Economics of Artificial Intelligence: An Agenda

Citations

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Cited by:

  1. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Romero Morales, Dolores, 2020. "Sparsity in optimal randomized classification trees," European Journal of Operational Research, Elsevier, vol. 284(1), pages 255-272.
  2. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
  3. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
  4. Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2022. "Peer Effects in Product Adoption," American Economic Journal: Applied Economics, American Economic Association, vol. 14(3), pages 488-526, July.
  5. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
  6. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
  7. Li, Jiyuan & Li, Zihui & Zhang, Min, 2023. "CFOs’ facial trustworthiness and bank loan contracts," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 332-357.
  8. Koffi Dumor & Li Yao, 2019. "Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
  9. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
  10. Daniel Goller & Michael C. Knaus & Michael Lechner & Gabriel Okasa, 2021. "Predicting match outcomes in football by an Ordered Forest estimator," Chapters, in: Ruud H. Koning & Stefan Kesenne (ed.), A Modern Guide to Sports Economics, chapter 22, pages 335-355, Edward Elgar Publishing.
  11. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
  12. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  13. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
  14. Plakandaras, Vasilios & Gogas, Periklis & Papadimitriou, Theophilos & Gupta, Rangan, 2019. "A re-evaluation of the term spread as a leading indicator," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 476-492.
  15. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
  16. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
  17. Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
  18. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
  19. Raaz Dwivedi & Yan Shuo Tan & Briton Park & Mian Wei & Kevin Horgan & David Madigan & Bin Yu, 2020. "Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 135-178, December.
  20. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  21. Tinglong Dai & Kelly Gleason & Chao‐Wei Hwang & Patricia Davidson, 2021. "Heart analytics: Analytical modeling of cardiovascular care," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 30-43, February.
  22. Mathias Bärtl & Simone Krummaker, 2020. "Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques," Risks, MDPI, vol. 8(1), pages 1-27, March.
  23. Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
  24. Robertas Damasevicius, 2023. "Progress, Evolving Paradigms and Recent Trends in Economic Analysis," Financial Economics Letters, Anser Press, vol. 2(2), pages 35-47, October.
  25. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
  26. Koffi Dumor & Komlan Gbongli, 2021. "Trade impacts of the New Silk Road in Africa: Insight from Neural Networks Analysis," Theory Methodology Practice (TMP), Faculty of Economics, University of Miskolc, vol. 17(02), pages 13-26.
  27. Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," CESifo Working Paper Series 8702, CESifo.
  28. Michael Allan Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers 1906.03044, arXiv.org.
  29. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
  30. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  31. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
  32. Ritu Agarwal & Michelle Dugas & Guodong (Gordon) Gao, 2024. "Augmenting physicians with artificial intelligence to transform healthcare: Challenges and opportunities," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 360-374, March.
  33. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
  34. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
  35. Paolo Brunori & Vito Peragine & Laura Serlenga, 2019. "Upward and downward bias when measuring inequality of opportunity," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(4), pages 635-661, April.
  36. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
  37. Kea BARET & Theophilos PAPADIMITRIOU, 2019. "On the Stability and Growth Pact compliance: what is predictable with machine learning?," Working Papers of BETA 2019-48, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  38. Brand, Claus & Ferrante, Lorenzo & Hubert, Antoine, 2019. "From cash- to securities-driven euro area repo markets: the role of financial stress and safe asset scarcity," Working Paper Series 2232, European Central Bank.
  39. Jeffrey P. Clemens & Parker Rogers, 2020. "Demand Shocks, Procurement Policies, and the Nature of Medical Innovation: Evidence from Wartime Prosthetic Device Patents," CESifo Working Paper Series 8781, CESifo.
  40. Ron Tidhar & Kathleen M. Eisenhardt, 2020. "Get rich or die trying… finding revenue model fit using machine learning and multiple cases," Strategic Management Journal, Wiley Blackwell, vol. 41(7), pages 1245-1273, July.
  41. Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.
  42. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
  43. De Lombaerde, Philippe & Naeher, Dominik & Vo, Hung Trung & Saber, Takfarinas, 2023. "Regional economic integration and machine learning: Policy insights from the review of literature," Journal of Policy Modeling, Elsevier, vol. 45(5), pages 1077-1097.
  44. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
  45. John Aoga & Juhee Bae & Stefanija Veljanoska & Siegfried Nijssen & Pierre Schaus, 2020. "Impact of weather factors on migration intention using machine learning algorithms," Papers 2012.02794, arXiv.org.
  46. Michael T. Kiley, 2020. "Financial Conditions and Economic Activity: Insights from Machine Learning," Finance and Economics Discussion Series 2020-095, Board of Governors of the Federal Reserve System (U.S.).
  47. 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).
  48. Anthony Strittmatter, 2018. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," Papers 1812.06533, arXiv.org, revised Dec 2021.
  49. Hazar Altınbaş & Vincenzo Pacelli & Edgardo Sica, 2022. "An Empirical Assessment of the Contagion Determinants in the Euro Area in a Period of Sovereign Debt Risk," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 339-371, July.
  50. Di Fang & Michael R. Thomsen & Rodolfo M. Nayga & Aaron M. Novotny, 2019. "WIC Participation and Relative Quality of Household Food Purchases: Evidence from FoodAPS," Southern Economic Journal, John Wiley & Sons, vol. 86(1), pages 83-105, July.
  51. Kindel, Alexander & Bansal, Vineet & Catena, Kristin & Hartshorne, Thomas & Jaeger, Kate & Koffman, Dawn & McLanahan, Sara & Phillips, Maya & Rouhani, Shiva & Vinh, Ryan, 2018. "Improving metadata infrastructure for complex surveys: Insights from the Fragile Families Challenge," SocArXiv u8spj, Center for Open Science.
  52. 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.
  53. Youssef M. Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2021. "Discrete Choice Analysis with Machine Learning Capabilities," Papers 2101.10261, arXiv.org.
  54. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
  55. Bessen, James & Impink, Stephen Michael & Reichensperger, Lydia & Seamans, Robert, 2022. "The role of data for AI startup growth," Research Policy, Elsevier, vol. 51(5).
  56. Rui (Aruhan) Shi, 2021. "Learning from Zero: How to Make Consumption-Saving Decisions in a Stochastic Environment with an AI Algorithm," CESifo Working Paper Series 9255, CESifo.
  57. Brieland, Stephanie & Töpfer, Marina, 2020. "The gender pay gap revisited: Does machine learning offer new insights?," Discussion Papers 111, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics.
  58. Panos Constantinides & Ola Henfridsson & Geoffrey G. Parker, 2018. "Introduction—Platforms and Infrastructures in the Digital Age," Information Systems Research, INFORMS, vol. 29(2), pages 381-400, June.
  59. Moreno Badia, Marialuz & Medas, Paulo & Gupta, Pranav & Xiang, Yuan, 2022. "Debt is not free," Journal of International Money and Finance, Elsevier, vol. 127(C).
  60. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  61. Born, Andreas & Janssen, Aljoscha, 2020. "Does a District-Vote Matter for the Behavior of Politicians? A Textual Analysis of Parliamentary Speeches," Working Paper Series 1320, Research Institute of Industrial Economics.
  62. Rui & Shi, 2021. "Learning from zero: how to make consumption-saving decisions in a stochastic environment with an AI algorithm," Papers 2105.10099, arXiv.org, revised Feb 2022.
  63. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
  64. Mara Lederman, 2018. "Comment on "The Impact of Machine Learning on Economics"," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 548-551, National Bureau of Economic Research, Inc.
  65. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
  66. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
  67. Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023. "Big data forecasting of South African inflation," Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
  68. René Böheim & Philipp Stöllinger, 2021. "Decomposition of the gender wage gap using the LASSO estimator," Applied Economics Letters, Taylor & Francis Journals, vol. 28(10), pages 817-828, June.
  69. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
  70. Manav Raj & Robert Seamans, 2019. "Primer on artificial intelligence and robotics," Journal of Organization Design, Springer;Organizational Design Community, vol. 8(1), pages 1-14, December.
  71. Pablo Picardo, 2019. "Predicción de precios de vivienda: Aprendizaje estadístico con datos de oferta y transacciones para la ciudad de Montevideo," Documentos de trabajo 2019002, Banco Central del Uruguay.
  72. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Morales, Dolores Romero, 2022. "On sparse optimal regression trees," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1045-1054.
  73. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
  74. Pawel Dlotko & Simon Rudkin & Wanling Qiu, 2019. "Topologically Mapping the Macroeconomy," Papers 1911.10476, arXiv.org.
  75. Paolo Brunori & Paul Hufe & Daniel Mahler, 2023. "The roots of inequality: estimating inequality of opportunity from regression trees and forests," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(4), pages 900-932, October.
  76. Li, Qing & Yu, Shuai & Échevin, Damien & Fan, Min, 2022. "Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
  77. Markus Eyting, 2020. "A Random Forest a Day Keeps the Doctor Away," Working Papers 2026, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
  78. Verhagen, Mark D., 2021. "Identifying and Improving Functional Form Complexity: A Machine Learning Framework," SocArXiv bka76, Center for Open Science.
  79. Smith, Gary, 2019. "The Paradox of Big Data," Economics Department, Working Paper Series 1003, Economics Department, Pomona College, revised 04 Jun 2019.
  80. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
  81. Isabel Hovdahl, 2019. "On the use of machine learning for causal inference in climate economics," Working Papers No 05/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  82. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
  83. Brunori, Paolo & Hufe, Paul & Mahler, Daniel Gerszon, 2021. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees and Forests," IZA Discussion Papers 14689, Institute of Labor Economics (IZA).
  84. Sinéad Keogh & Stephen O’Neill & Kieran Walsh, 2021. "Composite Measures for Assessing Multidimensional Social Exclusion in Later Life: Conceptual and Methodological Challenges," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(2), pages 389-410, June.
  85. Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021. "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, vol. 48(C).
  86. Wang, Xin (Shane) & Ryoo, Jun Hyun (Joseph) & Bendle, Neil & Kopalle, Praveen K., 2021. "The role of machine learning analytics and metrics in retailing research," Journal of Retailing, Elsevier, vol. 97(4), pages 658-675.
  87. Eslam Satarzadeh & Amirpouya Sarraf & Hooman Hajikandi & Mohammad Sadegh Sadeghian, 2022. "Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models," 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. 111(2), pages 1355-1373, March.
  88. Xuqi Chen & Yan Heng & Zhifeng Gao & Yuan Jiang, 2022. "Impacts of duo‐regional generic advertising of social media on consumer preference," Agribusiness, John Wiley & Sons, Ltd., vol. 38(1), pages 21-44, January.
  89. Bauer, Kevin & Pfeuffer, Nicolas & Abdel-Karim, Benjamin M. & Hinz, Oliver & Kosfeld, Michael, 2020. "The terminator of social welfare? The economic consequences of algorithmic discrimination," SAFE Working Paper Series 287, Leibniz Institute for Financial Research SAFE.
  90. Pollack, Adam B. & Kaufmann, Robert K., 2022. "Increasing storm risk, structural defense, and house prices in the Florida Keys," Ecological Economics, Elsevier, vol. 194(C).
  91. Born, Andreas & Janssen, Aljoscha, 2022. "Does a district mandate matter for the behavior of politicians? An analysis of roll-call votes and parliamentary speeches," European Journal of Political Economy, Elsevier, vol. 71(C).
  92. Stuart Gabriel & Matteo Iacoviello & Chandler Lutz, 2021. "A Crisis of Missed Opportunities? Foreclosure Costs and Mortgage Modification During the Great Recession [Synthetic control methods for comparative case studies: Estimating the effect of California," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 864-906.
  93. Carlos Mendez, 2019. "Lack of Global Convergence and the Formation of Multiple Welfare Clubs across Countries: An Unsupervised Machine Learning Approach," Economies, MDPI, vol. 7(3), pages 1-17, July.
  94. Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.
  95. Boger, Tal, 2018. "Forecasting Inflation in Iran by Applying Maching Learning Algorithms to PPP Lag," Studies in Applied Economics 126, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
  96. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
  97. Herrera, Diego & Cunniff, Shannon & DuPont, Carolyn & Cohen, Benjamin & Gangi, Dakota & Kar, Devyani & Peyronnin Snider, Natalie & Rojas, Victor & Wyerman, Jim & Norriss, Jessie & Mountenot, Marshall, 2019. "Designing an environmental impact bond for wetland restoration in Louisiana," Ecosystem Services, Elsevier, vol. 35(C), pages 260-276.
  98. Alexander Kindel & Vineet Bansal & Kristin Catena & Thomas Hartshorne & Kate Jaeger, 2018. "Improving metadata infrastructure for complex surveys: 
Insights from the Fragile Families Challenge," Working Papers wp18-10-ff, Princeton University, School of Public and International Affairs, Center for Research on Child Wellbeing..
  99. Thomas H. McInish & Olena Nikolsko‐Rzhevska & Alex Nikolsko‐Rzhevskyy & Irina Panovska, 2020. "Fast and slow cancellations and trader behavior," Financial Management, Financial Management Association International, vol. 49(4), pages 973-996, December.
  100. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-50, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  101. Heigle, Julia & Pfeiffer, Friedhelm, 2019. "An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 222378, March.
  102. Yacoubou Djima, Ismael & Kilic, Talip, 2024. "Attenuating measurement errors in agricultural productivity analysis by combining objective and self-reported survey data," Journal of Development Economics, Elsevier, vol. 168(C).
  103. Maria-Carmen García-Centeno & Román Mínguez-Salido & Raúl del Pozo-Rubio, 2021. "The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
  104. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
  105. repec:hal:wpaper:hal-03121966 is not listed on IDEAS
  106. Xie, Wen-Jie & Wei, Na & Zhou, Wei-Xing, 2023. "An interpretable machine-learned model for international oil trade network," Resources Policy, Elsevier, vol. 82(C).
  107. Vu, Khoa & Vuong, Nguyen Dinh Tuan & Vu-Thanh, Tu-Anh & Nguyen, Anh Ngoc, 2022. "Income shock and food insecurity prediction Vietnam under the pandemic," World Development, Elsevier, vol. 153(C).
  108. Joey Blumberg & Gary Thompson, 2022. "Nonparametric segmentation methods: Applications of unsupervised machine learning and revealed preference," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 976-998, May.
  109. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  110. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
  111. Boubacar Diallo, 2022. "Machine learning approaches to testing institutional hypotheses: the case of Acemoglu, Johnson, and Robinson (2001)," Empirical Economics, Springer, vol. 62(5), pages 2587-2600, May.
  112. Longbing Cao, 2021. "AI in Finance: Challenges, Techniques and Opportunities," Papers 2107.09051, arXiv.org.
  113. Philipp Mohl & Gilles Mourre & Sven Langedijk & Martijn Hoogeland, 2021. "Does Media Visibility Make EU Fiscal Rules More Effective?," European Economy - Discussion Papers 155, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
  114. Fabio Pammolli & Paolo Bonaretti & Massimo Riccaboni & Valentina Tortolini, 2019. "Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte," Working Papers CERM 01-2019, Competitività, Regole, Mercati (CERM).
  115. Thierno Bocar Diop & Lionel Védrine, 2025. "Did crop diversity criterion from CAP green payments affect both economic and environmental farm performances? Quasi-experimental evidence from France," Post-Print hal-04739921, HAL.
  116. Juhee Bae & John Aoga & Stefanija Veljanoska & Siegfried Nijssen & Pierre Schaus, 2020. "Impact of Weather Factors on Migration Intention using Machine Learning Algorithms," LIDAM Discussion Papers IRES 2020034, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
  117. Koffi Dumor & Li Yao & Jean-Paul Ainam & Edem Koffi Amouzou & Williams Ayivi, 2021. "Quantitative Dynamics Effects of Belt and Road Economies Trade Using Structural Gravity and Neural Networks," SAGE Open, , vol. 11(3), pages 21582440211, July.
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