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Redirect the Probability Approach in Econometrics Towards PAC Learning

Author

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  • 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 The bottleneck is traced to two key factors – a communal nescience of the theoretical foundation of ML and an outdated probability foundation.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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. I. Gilboa & A. Postlewaite & L. Samuelson & D. Schmeidler, 2015. "Economic models as analogies," Voprosy Ekonomiki, NP Voprosy Ekonomiki, issue 4.
    5. 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.
    6. Duo Qin, 2014. "Inextricability of Autonomy and Confluence in Econometrics," Working Papers 189, Department of Economics, SOAS University of London, UK.
    7. 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.
    8. 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.
    9. Emi Nakamura & Jón Steinsson, 2018. "Identification in Macroeconomics," Journal of Economic Perspectives, American Economic Association, vol. 32(3), pages 59-86, Summer.
    10. 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.
    11. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    12. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    13. 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.
    14. 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.
    15. 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.
    16. Robin Rowley & Omar Hamouda, 1987. "Troublesome Probability and Economics," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 10(1), pages 44-64, September.
    17. Hendry,David F. & Morgan,Mary S., 1997. "The Foundations of Econometric Analysis," Cambridge Books, Cambridge University Press, number 9780521588706, November.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. Qin, Duo, 2013. "A History of Econometrics: The Reformation from the 1970s," OUP Catalogue, Oxford University Press, number 9780199679348, Decembrie.
    23. 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.
    24. 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.
    Full references (including those not matched with items on IDEAS)

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    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

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