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The miracle of the Septuagint and the promise of data mining in economics


  • Stan du Plessis

    () (Department of Economics, University of Stellenbosch)


This paper argues that the sometimes-conflicting results of a modern revisionist literature on data mining in econometrics reflect different approaches to solving the central problem of model uncertainty in a science of non-experimental data. The literature has entered an exciting phase with theoretical development, methodological reflection, considerable technological strides on the computing front and interesting empirical applications providing momentum for this branch of econometrics. The organising principle for this discussion of data mining is a philosophical spectrum that sorts the various econometric traditions according to their epistemological assumptions (about the underlying data-generating-process DGP) starting with nihilism at one end and reaching claims of encompassing the DGP at the other end; call it the DGP-spectrum. In the course of exploring this spectrum the reader will encounter various Bayesian, specific-to-general (S-G) as well general-to-specific (G-S) methods. To set the stage for this exploration the paper starts with a description of data mining, its potential risks and a short section on potential institutional safeguards to these problems.

Suggested Citation

  • Stan du Plessis, 2006. "The miracle of the Septuagint and the promise of data mining in economics," Working Papers 15/2006, Stellenbosch University, Department of Economics.
  • Handle: RePEc:sza:wpaper:wpapers29

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    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Michele Gori & Vinicio Guidi, 2011. "Rhetoric and Conceptual Problems in Economics: the Case of General Equilibrium Theory," Working Papers - Economics wp2011_09.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    2. Meszaros, Sandor, 2008. "Theory testing (hypothesis testing) in agricultural economics," Studies in Agricultural Economics, Research Institute for Agricultural Economics, issue 107, March.

    More about this item


    Data mining; model selection; automated model selection; general to specific modelling; extreme bounds analysis; Bayesian model selection;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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