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Machine Learning Methods Economists Should Know About

Citations

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Cited by:

  1. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  2. Nicolas Gavoille & Anna Zasova, 2021. "What we pay in the shadow: Labor tax evasion, minimum wage hike and employment," Working Papers CEB 21-017, ULB -- Universite Libre de Bruxelles.
  3. 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.
  4. Blankenship, Brian & Aklin, Michaël & Urpelainen, Johannes & Nandan, Vagisha, 2022. "Jobs for a just transition: Evidence on coal job preferences from India," Energy Policy, Elsevier, vol. 165(C).
  5. Matthew A. Cole & Robert J R Elliott & Bowen Liu, 2020. "The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 553-580, August.
  6. Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
  7. Giacomo De Giorgi & Costanza Naguib, 2022. "Life after Default: Credit Hardship and its Effects," Diskussionsschriften dp2206, Universitaet Bern, Departement Volkswirtschaft.
  8. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
  9. James Ming Chen, 2021. "An Introduction to Machine Learning for Panel Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 27(1), pages 1-16, February.
  10. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
  11. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
  12. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
  13. Ravi Kumar & Shahin Boluki & Karl Isler & Jonas Rauch & Darius Walczak, 2022. "Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing," Papers 2205.01875, arXiv.org, revised Dec 2022.
  14. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  15. Brunori, Paolo & Salas-Rojo, Pedro & Verme, Paolo, 2022. "Estimating Inequality with Missing Incomes," GLO Discussion Paper Series 1138, Global Labor Organization (GLO).
  16. Ceriani, Lidia & Hlasny, Vladimir & Verme, Paolo, 2021. "Bottom Incomes and the Measurement of Poverty: A Brief Assessment of the Literature," GLO Discussion Paper Series 914, Global Labor Organization (GLO).
  17. Jau-er Chen & Chen-Wei Hsiang, 2019. "Causal Random Forests Model Using Instrumental Variable Quantile Regression," Econometrics, MDPI, vol. 7(4), pages 1-22, December.
  18. 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.
  19. 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).
  20. Horky, Florian & Rachel, Carolina & Fidrmuc, Jarko, 2022. "Price determinants of non-fungible tokens in the digital art market," Finance Research Letters, Elsevier, vol. 48(C).
  21. Baaken, Dominik & Hess, Sebastian, 2021. "Forecasting Regional Milk Production Quantity: A Comparison of Regression Models and Machine Learning," 2021 Conference, August 17-31, 2021, Virtual 315117, International Association of Agricultural Economists.
  22. Leonardo Cei & Edi Defrancesco & Gianluca Stefani, 2022. "What topic modelling can show about the development of agricultural economics: evidence from the Journal Citation Report category top journals," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(2), pages 289-330.
  23. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
  24. Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2020. "Neural Network Models for Empirical Finance," JRFM, MDPI, vol. 13(11), pages 1-22, October.
  25. Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2020. "Academic Scholarship in Light of the 2008 Financial Crisis: Textual Analysis of NBER Working Papers," EconStor Preprints 214194, ZBW - Leibniz Information Centre for Economics.
  26. Verme, Paolo, 2020. "Which Model for Poverty Predictions?," GLO Discussion Paper Series 468, Global Labor Organization (GLO).
  27. Mehmet Güney Celbiş & Pui‐hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 559-584, April.
  28. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
  29. Daniel Goller & Tamara Harrer & Michael Lechner & Joachim Wolff, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Papers 2106.10141, arXiv.org, revised May 2023.
  30. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
  31. 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.
  32. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
  33. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
  34. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
  35. 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.
  36. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  37. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).
  38. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  39. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
  40. Gert Bijnens & Shyngys Karimov & Jozef Konings, 2023. "Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach," De Economist, Springer, vol. 171(1), pages 85-117, March.
  41. Augustine Denteh & Helge Liebert, 2022. "Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment," Papers 2201.07072, arXiv.org, revised Apr 2023.
  42. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
  43. Boot, Arnoud & Hoffmann, Peter & Laeven, Luc & Ratnovski, Lev, 2020. "Financial intermediation and technology: What’s old, what’s new?," Working Paper Series 2438, European Central Bank.
  44. Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021. "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, vol. 53(52), pages 5989-6001, November.
  45. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
  46. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
  47. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
  48. Sonan Memon, 2021. "Machine Learning for Economists: An Introduction," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 60(2), pages 201-211.
  49. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
  50. Uguccioni, James, 2022. "The long-run effects of parental unemployment in childhood," CLEF Working Paper Series 45, Canadian Labour Economics Forum (CLEF), University of Waterloo.
  51. Dang,Hai-Anh H. & Kilic,Talip & Hlasny,Vladimir & Abanokova,Ksenia & Carletto,Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost : Evidence from a Randomized Survey Experiment," Policy Research Working Paper Series 10738, The World Bank.
  52. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
  53. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
  54. Costola, Michele & Hinz, Oliver & Nofer, Michael & Pelizzon, Loriana, 2023. "Machine learning sentiment analysis, COVID-19 news and stock market reactions," Research in International Business and Finance, Elsevier, vol. 64(C).
  55. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers 26165, National Bureau of Economic Research, Inc.
  56. Vladimir Hlasny & Lidia Ceriani & Paolo Verme, 2022. "Bottom Incomes and the Measurement of Poverty and Inequality," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(4), pages 970-1006, December.
  57. Tejas Ramdas & Martin T. Wells, 2024. "Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets," Papers 2409.05192, arXiv.org.
  58. Levy, Daniel & Mayer, Tamir & Raviv, Alon, 2022. "Economists in the 2008 financial crisis: Slow to see, fast to act," Journal of Financial Stability, Elsevier, vol. 60(C).
  59. Lee, Wang-Sheng & Tran, Trang My & Yu, Lamont Bo, 2023. "Green infrastructure and air pollution: Evidence from highways connecting two megacities in China," Journal of Environmental Economics and Management, Elsevier, vol. 122(C).
  60. Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
  61. Wang Guan-Yuan, 2021. "The Brand Effect: A Case Study in Taiwan Second-Hand Smartphone Market," Journal of Social and Economic Statistics, Sciendo, vol. 10(1-2), pages 30-42, December.
  62. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
  63. Ahlfeldt, Gabriel M. & Heblich, Stephan & Seidel, Tobias, 2023. "Micro-geographic property price and rent indices," Regional Science and Urban Economics, Elsevier, vol. 98(C).
  64. Nikos Askitas & Nikolaos Askitas, 2024. "A Hands-On Machine Learning Primer for Social Scientists: Math, Algorithms and Code," CESifo Working Paper Series 11353, CESifo.
  65. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  66. Igor Sadoune & Andrea Lodi & Marcelin Joanis, 2022. "Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data," Papers 2207.12255, arXiv.org, revised Feb 2024.
  67. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
  68. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2023. "Optimal Price Targeting," Marketing Science, INFORMS, vol. 42(3), pages 476-499, May.
  69. Brown, David P. & Cajueiro, Daniel O. & Eckert, Andrew & Silveira, Douglas, 2023. "Information and Transparency: Using Machine Learning to Detect Communication," Working Papers 2023-6, University of Alberta, Department of Economics.
  70. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
  71. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
  72. 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.
  73. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
  74. Andrew Tsang, 2024. "Uncovering heterogeneous regional impacts of Chinese monetary policy," Empirical Economics, Springer, vol. 67(3), pages 915-940, September.
  75. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
  76. Aysegül Kayaoglu & Ghassan Baliki & Tilman Brück & Melodie Al Daccache & Dorothee Weiffen, 2023. "How to conduct impact evaluations in humanitarian and conflict settings," HiCN Working Papers 387, Households in Conflict Network.
  77. Daniel Goller & Andrea Diem & Stefan C. Wolter, 2022. "Sitting next to a dropout: Study success of students with peers that came to the lecture hall by a different route," Economics of Education Working Paper Series 0190, University of Zurich, Department of Business Administration (IBW).
  78. Hurley, James & Karmakar, Sudipto & Markoska, Elena & Walczak, Eryk & Walker, Danny, 2021. "Impacts of the Covid-19 crisis: evidence from 2 million UK SMEs," Bank of England working papers 924, Bank of England.
  79. Feras A. Batarseh & Munisamy Gopinath & Anderson Monken & Zhengrong Gu, 2021. "Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning," Papers 2111.07508, arXiv.org.
  80. Zhexiao Lin & Fang Han, 2022. "On regression-adjusted imputation estimators of the average treatment effect," Papers 2212.05424, arXiv.org, revised Jan 2023.
  81. Carlos Fernández-Loría & Foster Provost & Jesse Anderton & Benjamin Carterette & Praveen Chandar, 2023. "A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation," Information Systems Research, INFORMS, vol. 34(2), pages 786-803, June.
  82. Bowen Liu & John R. Bryson & Deniz Sevinc & Matthew A. Cole & Robert J. R. Elliott & Suzanne E. Bartington & William J. Bloss & Zongbo Shi, 2023. "Assessing the Impacts of Birmingham’s Clean Air Zone on Air Quality: Estimates from a Machine Learning and Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 86(1), pages 203-231, October.
  83. Verhagen, Mark D., 2023. "Using machine learning to monitor the equity of large-scale policy interventions: The Dutch decentralisation of the Social Domain," SocArXiv qzm7y, Center for Open Science.
  84. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
  85. Bosker, Maarten, 2022. "City origins," Regional Science and Urban Economics, Elsevier, vol. 94(C).
  86. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  87. Patrick Lehnert & Michael Niederberger & Uschi Backes-Gellner & Eric Bettinger, 2020. "Proxying Economic Activity with Daytime Satellite Imagery: Filling Data Gaps Across Time and Space," Economics of Education Working Paper Series 0165, University of Zurich, Department of Business Administration (IBW), revised Sep 2022.
  88. Tarsia, Romano, 2024. "Heterogeneous effects of weather shocks on firm economic performance," LSE Research Online Documents on Economics 124251, London School of Economics and Political Science, LSE Library.
  89. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 605-635.
  90. Hannes Wallimann & David Imhof & Martin Huber, 2023. "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1669-1720, December.
  91. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  92. Kuanhao Jiang & Rajarshi Mukherjee & Subhabrata Sen & Pragya Sur, 2022. "A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond," Papers 2205.10198, arXiv.org, revised Oct 2022.
  93. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
  94. Garg, Prashant & Fetzer, Thiemo, 2024. "Causal Claims in Economics," OSF Preprints u4vgs, Center for Open Science.
  95. Sarr, Ibrahima & Dang, Hai-Anh H. & Guzman Gutierrez, Carlos Santiago & Beltramo, Theresa & Verme, Paolo, 2024. "Using Cross-Survey Imputation to Estimate Poverty for Venezuelan Refugees in Colombia," GLO Discussion Paper Series 1534, Global Labor Organization (GLO).
  96. Laeven, Luc & Boot, Arnoud & Hoffmann, Peter & Ratnovski, Lev, 2020. "Financial Intermediation and Technology: What’s Old, What’s New?," CEPR Discussion Papers 15004, C.E.P.R. Discussion Papers.
  97. Kota Sugimoto, 2024. "Competitive effects of implicit auction on interconnectors: evidence from Japan," Journal of Regulatory Economics, Springer, vol. 66(1), pages 95-134, August.
  98. Harsh Parikh & Carlos Varjao & Louise Xu & Eric Tchetgen Tchetgen, 2022. "Validating Causal Inference Methods," Papers 2202.04208, arXiv.org, revised Jul 2022.
  99. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
  100. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
  101. Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series 2104, University of St. Gallen, School of Economics and Political Science.
  102. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
  103. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
  104. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
  105. Maria Ana Matias & Rita Santos & Panos Kasteridis & Katja Grasic & Anne Mason & Nigel Rice, 2022. "Approaches to projecting future healthcare demand," Working Papers 186cherp, Centre for Health Economics, University of York.
  106. Bessen, James & Impink, Stephen Michael & Reichensperger, Lydia & Seamans, Robert, 2022. "The role of data for AI startup growth," Research Policy, Elsevier, vol. 51(5).
  107. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
  108. Nathapornpan Uttama & Popkarn Arwatchanakarn, 2023. "How do economic complexity and productive capacities foster foreign direct investment flows? Evidence from the Asian economies," Economics Bulletin, AccessEcon, vol. 43(1), pages 629-643.
  109. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2022. "Multiway Cluster Robust Double/Debiased Machine Learning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1046-1056, June.
  110. Arellano-Bover, Jaime & Saltiel, Fernando, 2021. "Differences in On-the-Job Learning across Firms," IZA Discussion Papers 14473, Institute of Labor Economics (IZA).
  111. Chen, Ruoyu & Jiang, Hanchen & Quintero, Luis E., 2023. "Measuring the value of rent stabilization and understanding its implications for racial inequality: Evidence from New York City," Regional Science and Urban Economics, Elsevier, vol. 103(C).
  112. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
  113. Donna B. Gilleskie, 2021. "In sickness and in health, until death do us part: A case for theory," Southern Economic Journal, John Wiley & Sons, vol. 87(3), pages 753-768, January.
  114. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
  115. Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023. "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics 2023_13, University of São Paulo (FEA-USP).
  116. Taiyo Fukai & Hidehiko Ichimura & Keisuke Kawata, 2021. "Describing the impacts of COVID-19 on the labor market in Japan until June 2020," The Japanese Economic Review, Springer, vol. 72(3), pages 439-470, July.
  117. Mark Musumba & Naureen Fatema & Shahriar Kibriya, 2021. "Prevention Is Better Than Cure: Machine Learning Approach to Conflict Prediction in Sub-Saharan Africa," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
  118. Dangxing Chen & Luyao Zhang, 2023. "Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance," Papers 2301.07060, arXiv.org.
  119. David Imhof & Hannes Wallimann, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," Papers 2105.00337, arXiv.org.
  120. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
  121. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  122. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
  123. Guan‐Yuan Wang, 2023. "The effect of environment on housing prices: Evidence from the Google Street View," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 288-311, March.
  124. Ay, Jean-Sauveur & Le Gallo, Julie, 2021. "The Signaling Values of Nested Wine Names," Working Papers 321851, American Association of Wine Economists.
  125. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
  126. De Giorgi, Giacomo & Naguib, Costanza, 2024. "Life after (soft) default," European Economic Review, Elsevier, vol. 167(C).
  127. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
  128. Lampach, Nicolas & To-The, Nguyen & Nguyen-Anh, Tuan, 2021. "Technical efficiency and the adoption of multiple agricultural technologies in the mountainous areas of Northern Vietnam," Land Use Policy, Elsevier, vol. 103(C).
  129. Roberto Esposti, 2022. "Non-Monetary Motivations Of Agroenvironmental Policies Adoption. A Causal Forest Approach," Working Papers 459, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
  130. Ballestar, María Teresa & Mir, Miguel Cuerdo & Pedrera, Luis Miguel Doncel & Sainz, Jorge, 2024. "Effectiveness of tutoring at school: A machine learning evaluation," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  131. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
  132. Su, Jiun-Hua, 2021. "Model selection in utility-maximizing binary prediction," Journal of Econometrics, Elsevier, vol. 223(1), pages 96-124.
  133. He, Ke & Ye, Lihong & Li, Fanlue & Chang, Huayi & Wang, Anbang & Luo, Sixuan & Zhang, Junbiao, 2022. "Using cognition and risk to explain the intention-behavior gap on bioenergy production: Based on machine learning logistic regression method," Energy Economics, Elsevier, vol. 108(C).
  134. Ballinari, Daniele, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Economics Letters, Elsevier, vol. 241(C).
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  179. Arenas, Andreu & Calsamiglia, Caterina, 2022. "Gender Differences in High-Stakes Performance and College Admission Policies," IZA Discussion Papers 15550, Institute of Labor Economics (IZA).
  180. Urmat Dzhunkeev, 2022. "Forecasting Unemployment in Russia Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 81(1), pages 73-87, March.
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  201. 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).
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