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

Stan Du Plessis ()

No 15/2006, Working Papers from Stellenbosch University, Department of Economics

Abstract: 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.

Keywords: Data mining; model selection; automated model selection; general to specific modelling; extreme bounds analysis; Bayesian model selection (search for similar items in EconPapers)
JEL-codes: C11 C50 C51 C52 C87 (search for similar items in EconPapers)
Date: 2006
New Economics Papers: this item is included in nep-ecm and nep-rmg
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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