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Machine Learning Methods for Demand Estimation

Citations

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Cited by:

  1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
  2. Daniele Guariso, 2018. "Terrorist Attacks and Immigration Rhetoric: A Natural Experiment on British MPs," Working Paper Series 1218, Department of Economics, University of Sussex Business School.
  3. Gogolev, Stepan & Ozhegov, Evgeniy, 2023. "Asymmetric loss function in product-level sales forecasting: An empirical comparison," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 109-121.
  4. Deimante Teresiene & Margarita Aleksynaite, 2020. "The Use of Technical Analysis in the US, European and Asian Stock Markets," Technium Social Sciences Journal, Technium Science, vol. 8(1), pages 302-318, June.
  5. Federico Zincenko, 2023. "Nonparametric estimation of conditional densities by generalized random forests," Papers 2309.13251, arXiv.org, revised May 2024.
  6. Bonnet, Céline & Richards, Timothy J., 2016. "Models of Consumer Demand for Differentiated Products," TSE Working Papers 16-741, Toulouse School of Economics (TSE).
  7. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
  8. Merino Troncoso, Carlos, 2021. "Consumer Demand Estimation," MPRA Paper 105169, University Library of Munich, Germany.
  9. Khudri, Md Mohsan & Hussey, Andrew, 2024. "Breastfeeding and Child Development Outcomes across Early Childhood and Adolescence: Doubly Robust Estimation with Machine Learning," IZA Discussion Papers 17080, Institute of Labor Economics (IZA).
  10. Luo, Ye & Spindler, Martin & Bach, Philipp, 2019. "Dynamic Pricing mit Künstlicher Intelligenz - Fallstudie aus dem Ride-Sharing-Markt," Marketing Review St.Gallen, Universität St.Gallen, Institut für Marketing und Customer Insight, vol. 36(5), pages 48-54.
  11. Miriam Steurer & Robert Hill, 2019. "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers 2019-02, University of Graz, Department of Economics.
  12. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
  13. Badruddoza, Syed & Amin, Modhurima & McCluskey, Jill, 2019. "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers 2019-5, School of Economic Sciences, Washington State University.
  14. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
  15. 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.
  16. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
  17. Phoebe Koundouri & Barbara Hammer & Ulrike Kuhl & Alina Velias, 2022. "Behavioral and Neuroeconomics of Environmental Values," DEOS Working Papers 2227, Athens University of Economics and Business.
  18. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
  19. repec:ags:aaea22:335946 is not listed on IDEAS
  20. Zhan Gao & Zhentao Shi, 2021. "Implementing Convex Optimization in R: Two Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1127-1135, December.
  21. Marica Valente & Timm Gries & Lorenzo Trapani, 2023. "Informal employment from migration shocks," Working Papers 2023-09, Faculty of Economics and Statistics, Universität Innsbruck.
  22. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
  23. Marc Bourreau & Yutec Sun, 2022. "Competition and Quality: Evidence from the Entry of Mobile Network Service," Working Papers 22-04, NET Institute.
  24. Zhu, Manhong & Schmitz, Andrew & Schmitz, Troy G., "undated". "What are the Culprits Causing Obesity? A Machine Learning Approach in Variable Selection and Parameter Coefficient Inference," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 261220, Agricultural and Applied Economics Association.
  25. Steven Lehrer & Tian Xie & Tao Zeng, 2021. "Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures? [Regression Models with Mixed Sampling Frequencies]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 910-933.
  26. Frankel, Richard & Jennings, Jared & Lee, Joshua, 2016. "Using unstructured and qualitative disclosures to explain accruals," Journal of Accounting and Economics, Elsevier, vol. 62(2), pages 209-227.
  27. Jonathan Leslie, 2023. "?Seeing? the Future: Improving Macroeconomic Forecasts with Spatial Data and Recurrent Convolutional Neural Networks," CAEPR Working Papers 2023-003 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
  28. Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
  29. Amin, Modhurima Dey & Badruddoza, Syed & McCluskey, Jill J., 2021. "Predicting access to healthful food retailers with machine learning," Food Policy, Elsevier, vol. 99(C).
  30. Keaton Miller & Boyoung Seo, 2021. "The Effect of Cannabis Legalization on Substance Demand and Tax Revenues," National Tax Journal, University of Chicago Press, vol. 74(1), pages 107-145.
  31. Yanqing Yang & Xingcheng Xu & Jinfeng Ge & Yan Xu, 2024. "Machine Learning for Economic Forecasting: An Application to China's GDP Growth," Papers 2407.03595, arXiv.org.
  32. 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).
  33. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
  34. Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
  35. 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.
  36. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
  37. Uzma Mushtaque & Jennifer A. Pazour, 2020. "Random Utility Models with Cardinality Context Effects for Online Subscription Service Platforms," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(4), pages 276-290, August.
  38. Tsun Se Cheong & Guanghua Wan & David Kam Hung Chui, 2022. "Unveiling the Relationship between Economic Growth and Equality for Developing Countries," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 30(5), pages 1-28, September.
  39. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
  40. Merino Troncoso, Carlos, 2023. "Introduction to Competition Economics," MPRA Paper 115999, University Library of Munich, Germany.
  41. Dylan Brewer & Alyssa Carlson, 2024. "Addressing sample selection bias for machine learning methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 383-400, April.
  42. Evgeniy M. Ozhegov & Alina Ozhegova, 2020. "Regression tree model for prediction of demand with heterogeneity and censorship," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 489-500, April.
  43. Chengyan Gu, 2023. "Market segmentation and dynamic price discrimination in the U.S. airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(5), pages 338-361, October.
  44. Jorge Mejia & Shawn Mankad & Anandasivam Gopal, 2019. "A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections," Information Systems Research, INFORMS, vol. 30(4), pages 1363-1386, December.
  45. Xueling Li & Xiaoyan Zhang & Yuan Liu & Yuanying Mi & Yong Chen, 2022. "The impact of artificial intelligence on users' entrepreneurial activities," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 597-608, May.
  46. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
  47. Shiguang Li & Yixiang Tian, 2023. "How Does Digital Transformation Affect Total Factor Productivity: Firm-Level Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
  48. Emrich Eike & Pierdzioch Christian, 2016. "Public Goods, Private Consumption, and Human Capital: Using Boosted Regression Trees to Model Volunteer Labour Supply," Review of Economics, De Gruyter, vol. 67(3), pages 263-283, December.
  49. Evgeniy M. Ozhegov & Daria Teterina, 2018. "The Ensemble Method For Censored Demand Prediction," HSE Working papers WP BRP 200/EC/2018, National Research University Higher School of Economics.
  50. Pedro M. Gardete & Carlos D. Santos, 2020. "No data? No problem! A Search-based Recommendation System with Cold Starts," Papers 2010.03455, arXiv.org.
  51. Tzai-Shuen Chen, 2018. "Evaluating Conditional Cash Transfer Policies with Machine Learning Methods," Papers 1803.06401, arXiv.org.
  52. Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2022. "How effective is carbon pricing?—A machine learning approach to policy evaluation," Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
  53. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
  54. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
  55. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
  56. Sun, Sizhong, 2022. "The demand for a COVID-19 vaccine," Economics & Human Biology, Elsevier, vol. 46(C).
  57. Raval, Devesh & Rosenbaum, Ted & Wilson, Nathan E., 2021. "How do machine learning algorithms perform in predicting hospital choices? evidence from changing environments," Journal of Health Economics, Elsevier, vol. 78(C).
  58. Sunghyeon Choi & Jin Hur, 2020. "An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting," Energies, MDPI, vol. 13(6), pages 1-16, March.
  59. 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.
  60. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Économétrie & Machine Learning," Working Papers hal-01568851, HAL.
  61. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
  62. Embaye, Weldensie T. & Zereyesus, Yacob A., 2017. "Measuring the value of housing services in household surveys: an application of machine learning approach," 2017 Annual Meeting, February 4-7, 2017, Mobile, Alabama 252851, Southern Agricultural Economics Association.
  63. Evgeny A. Antipov & Elena B. Pokryshevskaya, 2020. "Interpretable machine learning for demand modeling with high-dimensional data using Gradient Boosting Machines and Shapley values," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 355-364, October.
  64. Zhang, Wenzhe & Liu, Guangqiang, 2023. "Digitalization and firm centralization: A quasi-natural experiment based on the “Broadband China” policy," Finance Research Letters, Elsevier, vol. 52(C).
  65. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
  66. Evgeniy M. Ozhegov & Alina Ozhegova, 2017. "Regression Tree Model for Analysis of Demand with Heterogeneity and Censorship," HSE Working papers WP BRP 174/EC/2017, National Research University Higher School of Economics.
  67. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).
  68. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020. "Deep Learning for Individual Heterogeneity: An Automatic Inference Framework," Papers 2010.14694, arXiv.org, revised Jul 2021.
  69. Haixiang Yao & Shenghao Xia & Hao Liu, 2024. "Return predictability via an long short‐term memory‐based cross‐section factor model: Evidence from Chinese stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1770-1794, September.
  70. Louis R. Nemzer & Florence Neymotin, 2020. "Concierge care and patient reviews," Health Economics, John Wiley & Sons, Ltd., vol. 29(8), pages 913-922, August.
  71. 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.
  72. Wenjie Bi & Bing Wang & Haiying Liu, 2024. "Personalized Dynamic Pricing Based on Improved Thompson Sampling," Mathematics, MDPI, vol. 12(8), pages 1-14, April.
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