arXiv 15 Nov 2021 · Econometrics · publishedJournal of Quantitative Economics (2022) · 1 citations (OpenAlex)
arXiv:2111.08054 · PDF · DOI · OpenAlex · Extracted main text
This paper is about two things: (i) Charles Sanders Peirce (1837-1914) -- an iconoclastic philosopher and polymath who is among the greatest of American minds. (ii) Abductive inference -- a term coined by C. S. Peirce, which he defined as "the process of forming explanatory hypotheses. It is the only logical operation which introduces any new idea." Abductive inference and quantitative economics: Abductive inference plays a fundamental role in empirical scientific research as a tool for discovery and data analysis. Heckman and Singer (2017) strongly advocated "Economists should abduct." Arnold Zellner (2007) stressed that "much greater emphasis on reductive [abductive] inference in teaching econometrics, statistics, and economics would be desirable." But currently, there are no established theory or practical tools that can allow an empirical analyst to abduct. This paper attempts to fill this gap by introducing new principles and concrete procedures to the Economics and Statistics community. I termed the proposed approach as Abductive Inference Machine (AIM). The historical Peirce's experiment: In 1872, Peirce conducted a series of experiments to determine the distribution of response times to an auditory stimulus, which is widely regarded as one of the most significant statistical investigations in the history of nineteenth-century American mathematical research (Stigler, 1978). On the 150th anniversary of this historical experiment, we look back at the Peircean-style abductive inference through a modern statistical lens. Using Peirce's data, it is shown how empirical analysts can abduct in a systematic and automated manner using AIM.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Heckman, J. J. and B. Singer (2017) Abducting economics | 0.644 | 2 | 2 | 100% |
| 2 | Mukhopadhyay, S (2017) Large-scale mode identification and data-driven sciences | 0.511 | 2 | 1 | 100% |
| 3 | Haavelmo, T (1944) The probability approach in econometrics | 0.511 | 2 | 1 | 100% |
| 4 | Peirce, C. S (1873) On the theory of errors of observation | 0.511 | 2 | 1 | 100% |
| 5 | Wilson, E. B. and M. M. Hilferty (1929) Note on C. \,S.\, Peirce's experimental discussion of the law of errors | 0.511 | 2 | 1 | 100% |
| 6 | Mukhopadhyay, S. and E. Parzen (2020) Nonparametric universal copula modeling | 0.405 | 1 | 1 | 100% |
| 7 | Mukhopadhyay, S (2022) A maximum entropy copula model for mixed data: Representation, estimation, and applications | 0.405 | 1 | 1 | 100% |
| 8 | Mukhopadhyay, S (2022) Modelplasticity and abductive decision making | 0.405 | 1 | 1 | 100% |
| 9 | Bailey, D. C (2017) Not normal: the uncertainties of scientific measurements | 0.405 | 1 | 1 | 100% |
| 10 | Box, G. E (1976) Science and Statistics | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.