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Three attitudes towards data mining

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  • Kevin Hoover
  • Stephen Perez

Abstract

'Data mining' refers to a broad class of activities that have in common, a search over different ways to process or package data statistically or econometrically with the purpose of making the final presentation meet certain design criteria. We characterize three attitudes toward data mining: first, that it is to be avoided and, if it is engaged in, that statistical inferences must be adjusted to account for it; second, that it is inevitable and that the only results of any interest are those that transcend the variety of alternative data mined specifications (a view associated with Leamer's extreme-bounds analysis); and third, that it is essential and that the only hope we have of using econometrics to uncover true economic relationships is to be found in the intelligent mining of data. The first approach confuses considerations of sampling distribution and considerations of epistemic warrant and, reaches an unnecessarily hostile attitude toward data mining. The second approach relies on a notion of robustness that has little relationship to truth: there is no good reason to expect a true specification to be robust alternative specifications. Robustness is not, in general, a carrier of epistemic warrant. The third approach is operationalized in the general-to-specific search methodology of the LSE school of econometrics. Its success demonstrates that intelligent data mining is an important element in empirical investigation in economics.

Suggested Citation

  • Kevin Hoover & Stephen Perez, 2001. "Three attitudes towards data mining," Journal of Economic Methodology, Taylor & Francis Journals, vol. 7(2), pages 195-210.
  • Handle: RePEc:taf:jecmet:v:7:y:2001:i:2:p:195-210 DOI: 10.1080/13501780050045083
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jim Woodward, 2006. "Some varieties of robustness," Journal of Economic Methodology, Taylor & Francis Journals, pages 219-240.
    2. Thomas Mayer, 2006. "The Empirical Significance of Econometric Models," Working Papers 620, University of California, Davis, Department of Economics.
    3. Thomas Mayer, "undated". "Misinterpreting a Failure to Disconfirm as a Confirmation: A Recurrent Misreading of Significance Tests," Department of Economics 01-08, California Davis - Department of Economics.
    4. Hsiang-Ke Chao, 2005. "A misconception of the semantic conception of econometrics?," Journal of Economic Methodology, Taylor & Francis Journals, pages 125-135.
    5. Kevin D. Hoover & Stephen J. Perez, 2004. "Truth and Robustness in Cross-country Growth Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 765-798, December.
    6. Kevin Hoover & Mark Siegler, 2008. "Sound and fury: McCloskey and significance testing in economics," Journal of Economic Methodology, Taylor & Francis Journals, pages 1-37.
    7. Thomas Mayer, 2009. "Honesty and Integrity in Economics," Working Papers 92, University of California, Davis, Department of Economics.
    8. Steven Cook, 2001. "Observations on the practice of data-mining: comments on the JEM symposium," Journal of Economic Methodology, Taylor & Francis Journals, pages 415-419.
    9. Christophe Schinckus, 2011. "What can econophysics contribute to financial economics?," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 58(2), pages 147-163, June.
    10. Boldyrev, I., 2011. "Economic Methodology Today: a Review of Major Contributions," Journal of the New Economic Association, New Economic Association, issue 9, pages 47-70.
    11. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.

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