IDEAS home Printed from https://ideas.repec.org/p/soa/wpaper/249.html
   My bibliography  Save this paper

Redirect the Probability Approach in Econometrics Towards PAC Learning

Author

Listed:
  • Duo Qin

    (Department of Economics, SOAS University of London)

Abstract

Infiltration of machine learning (ML) methods into econometrics has remained relatively slow, compared with their extensive applications in many other disciplines. The bottleneck is traced to two key factors – a communal nescience of the theoretical foundation of ML and an outdated probability foundation. The present study ventures on an overhaul of the probability approach by Haavelmo (1944) in light of ML theories of learnibility, centred upon the notion of probably approximately correct (PAC) learning. The study argues for a reorientation of the probability approach towards assisting decision making for model learning and selection purposes. The first part of the study is presented here.

Suggested Citation

  • Duo Qin, 2022. "Redirect the Probability Approach in Econometrics Towards PAC Learning," Working Papers 249, Department of Economics, SOAS University of London, UK.
    • Handle: RePEc:soa:wpaper:249
    as

    Download full text from publisher

    File URL: https://www.soas.ac.uk/sites/default/files/2022-10/economics-wp249.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin S. Eichenbaum, 1996. "Some comments on the role of econometrics in economic theory," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 20(Jan), pages 22-31.
    2. D. R. Cox, 1992. "Causality: Some Statistical Aspects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 155(2), pages 291-301, March.
    3. Paul Davidson, 1991. "Is Probability Theory Relevant for Uncertainty? A Post Keynesian Perspective," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 129-143, Winter.
    4. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Econometrics and Machine Learning," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 147-169.
    5. Robin Rowley & Omar Hamouda, 1987. "Troublesome Probability and Economics," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 10(1), pages 44-64, September.
    6. Krishna Rao & Argia M. Sbordone & Andrea Tambalotti & Kieran Walsh, 2010. "Policy analysis using DSGE models: an introduction," Economic Policy Review, Federal Reserve Bank of New York, vol. 16(Oct), pages 23-43.
    7. I. Gilboa & A. Postlewaite & L. Samuelson & D. Schmeidler, 2015. "Economic models as analogies," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 4.
    8. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
    9. Hendry,David F. & Morgan,Mary S., 1997. "The Foundations of Econometric Analysis," Cambridge Books, Cambridge University Press, number 9780521588706.
    10. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    11. Fedor Iskhakov & John Rust & Bertel Schjerning, 2020. "Machine learning and structural econometrics: contrasts and synergies," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 81-124.
    12. Duo Qin, 2014. "Inextricability of Autonomy and Confluence in Econometrics," Working Papers 189, Department of Economics, SOAS University of London, UK.
    13. Herman Wold, 1980. "Model Construction and Evaluation When Theoretical Knowledge Is Scarce," NBER Chapters, in: Evaluation of Econometric Models, pages 47-74, National Bureau of Economic Research, Inc.
    14. Robert J. Hill, 2013. "Hedonic Price Indexes For Residential Housing: A Survey, Evaluation And Taxonomy," Journal of Economic Surveys, Wiley Blackwell, vol. 27(5), pages 879-914, December.
    15. T. D. Stanley, 1998. "Empirical Economics? An Econometric Dilemma with Only a Methodological Solution," Journal of Economic Issues, Taylor & Francis Journals, vol. 32(1), pages 191-218, March.
    16. Gilbert, Christopher L, 1986. "Professor Hendry's Econometric Methodology," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 283-307, August.
    17. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    18. Qin, Duo, 2013. "A History of Econometrics: The Reformation from the 1970s," OUP Catalogue, Oxford University Press, number 9780199679348.
    19. Tomaso Poggio & Ryan Rifkin & Sayan Mukherjee & Partha Niyogi, 2004. "General conditions for predictivity in learning theory," Nature, Nature, vol. 428(6981), pages 419-422, March.
    20. Emi Nakamura & Jón Steinsson, 2018. "Identification in Macroeconomics," Journal of Economic Perspectives, American Economic Association, vol. 32(3), pages 59-86, Summer.
    21. Jack Triplett, 2004. "Handbook on Hedonic Indexes and Quality Adjustments in Price Indexes: Special Application to Information Technology Products," OECD Science, Technology and Industry Working Papers 2004/9, OECD Publishing.
    22. Pesaran, M. Hashem, 1987. "Global and Partial Non-Nested Hypotheses and Asymptotic Local Power," Econometric Theory, Cambridge University Press, vol. 3(1), pages 69-97, February.
    23. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    24. Fabio Canova, 2009. "How much Structure in Empirical Models?," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 2, pages 68-97, Palgrave Macmillan.
    Full references (including those not matched with items on IDEAS)

    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.
    1. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    2. Duo Qin, 2019. "Let’s take the bias out of econometrics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 26(2), pages 81-98, April.
    3. 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.
    4. 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.
    5. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    6. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    7. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    8. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    9. 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.
    10. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    11. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
    12. 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.
    13. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    14. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    15. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    16. 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).
    17. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    18. Robert J. Hill & Michael Scholz, 2014. "Incorporating Geospatial Data in House Price Indexes: A Hedonic Imputation Approach with Splines," Graz Economics Papers 2014-05, University of Graz, Department of Economics.
    19. Duo Qin, 2014. "Inextricability of Autonomy and Confluence in Econometrics," Working Papers 189, Department of Economics, SOAS University of London, UK.
    20. Ogundari, Kolawole, 2021. "A systematic review of statistical methods for estimating an education production function," MPRA Paper 105283, University Library of Munich, Germany.

    More about this item

    Keywords

    probability; uncertainty; machine learning; hypothesis testing; knowledge; representation;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    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:soa:wpaper:249. 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: Chandni Dwarkasing (email available below). General contact details of provider: https://edirc.repec.org/data/desoauk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.