The miracle of the Septuagint and the promise of data mining in economics
Stellenbosch Working Paper Series No. WP15/2006Publication date: 2006
Author(s):
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.
JEL Classification:C11, C50, C51, C52, C87
Keywords:Data mining, model selection, automated model selection, general to specific modelling, extreme bounds analysis, Bayesian model selection
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19 Apr 2024There was good news for global growth this week – with China's Q1 GDP beating expectations (see international section) and the IMF lifting its global growth forecast for 2024 once more. SA economic data releases, however, were mixed, with a welcome downtick in CPI inflation but relatively poor internal trade data. Most of the world’s economic policymakers...
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