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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 The bottleneck is traced to two key factors – a communal nescience of the theoretical foundation of ML and an outdated probability foundation.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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    File URL: https://www.soas.ac.uk/sites/default/files/2022-10/economics-wp249.pdf
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    References listed 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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