Interpreting Results of Demand Estimation from Machine Learning Models
Author
Abstract
Suggested Citation
DOI: 10.22004/ag.econ.236147
Download full text from publisher
References listed on IDEAS
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014.
"High-Dimensional Methods and Inference on Structural and Treatment Effects,"
Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.
- Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Demand Estimation with Machine Learning and Model Combination," NBER Working Papers 20955, National Bureau of Economic Research, Inc.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- David Mayer-Foulkes, 2018. "Efficient Urbanization for Mexican Development," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(10), pages 1-1, October.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Braaksma, Barteld & Zeelenberg, Kees, 2015. "“Re-make/Re-model”: Should big data change the modelling paradigm in official statistics?," MPRA Paper 87741, University Library of Munich, Germany.
- 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.
- Christophe Croux & Julapa Jagtiani & Tarunsai Korivi & Milos Vulanovic, 2020. "Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform," Working Papers 20-15, Federal Reserve Bank of Philadelphia.
- Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
- Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
- Cerulli, Giovanni, 2020. "A Super-Learning Machine for Predicting Economic Outcomes," MPRA Paper 99111, University Library of Munich, Germany.
- Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022.
"Predictably Unequal? The Effects of Machine Learning on Credit Markets,"
Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
- Goldsmith-Pinkham, Paul & Walther, Ansgar, 2017. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," CEPR Discussion Papers 12448, C.E.P.R. Discussion Papers.
- 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.
- Monica Andini & Michela Boldrini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Andrea Paladini, 2019. "Machine learning in the service of policy targeting: the case of public credit guarantees," Temi di discussione (Economic working papers) 1206, Bank of Italy, Economic Research and International Relations Area.
- Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2018.
"Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence,"
NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 89-110,
National Bureau of Economic Research, Inc.
- Ajay K. Agrawal & Joshua S. Gans & Avi Goldfarb, 2018. "Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence," NBER Working Papers 24243, National Bureau of Economic Research, Inc.
- Shengying Zhai & Qihui Chen & Wenxin Wang, 2019. "What Drives Green Fodder Supply in China?—A Nerlovian Analysis with LASSO Variable Selection," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
- Francesco Bloise & Paolo Brunori & Patrizio Piraino, 2021.
"Estimating intergenerational income mobility on sub-optimal data: a machine learning approach,"
The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 643-665, December.
- Francesco Bloise & Paolo Brunori & Patrizio Piraino, 2020. "Estimating intergenerational income mobility on sub-optimal data: a machine learning approach," Working Papers 526, ECINEQ, Society for the Study of Economic Inequality.
- 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.
- Erik Nelson & John Fitzgerald & Nathan Tefft, 2019. "The distributional impact of a green payment policy for organic fruit," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-25, February.
- James T. E. Chapman & Ajit Desai, 2023.
"Macroeconomic Predictions Using Payments Data and Machine Learning,"
Forecasting, MDPI, vol. 5(4), pages 1-32, November.
- James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
- James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
- 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.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- McKenzie, David & Sansone, Dario, 2017.
"Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria,"
CEPR Discussion Papers
12523, C.E.P.R. Discussion Papers.
- Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
- Joyce P Jacobsen & Laurence M Levin & Zachary Tausanovitch, 2016.
"Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists’ Predictions,"
Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 42(3), pages 387-398, June.
- Joyce P. Jacobsen & Laurence M. Levin & Zachary Tausanovitch, 2014. "Comparing Standard Regression Modeling to Ensemble Modeling: How Data Mining Software Can Improve Economists' Predictions," Wesleyan Economics Working Papers 2014-003, Wesleyan University, Department of Economics.
- Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
- Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022.
"Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach,"
Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
- Michael Knaus & Michael Lechner & Anthony Strittmatter, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Papers 1709.10279, arXiv.org, revised May 2018.
- Lechner, Michael & Strittmatter, Anthony & Knaus, Michael C., 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," CEPR Discussion Papers 12224, C.E.P.R. Discussion Papers.
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
- Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," Economics Working Paper Series 1711, University of St. Gallen, School of Economics and Political Science.
- Stolbov, Mikhail & Shchepeleva, Maria, 2020. "What predicts the legal status of cryptocurrencies?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 273-291.
- Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
More about this item
Keywords
Consumer/Household Economics; Demand and Price Analysis;NEP fields
This paper has been announced in the following NEP Reports:- NEP-HME-2016-06-14 (Heterodox Microeconomics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea16:236147. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.