Abstract and Keywords
Data mining could compromise the believability of econometric models. And yet there might not be an alternative to data mining if economics is going to be an empirical science practiced with the joint constraints of incomplete economic theory and non-experimental data. The organizing 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 instrumentalism at one end and reaching claims of encompassing the DGP at the other; call it the DGP-spectrum. In the course of exploring this spectrum, this article discusses various Bayesian, specific to general (S–G) as well as general to specific (G–S) methods. A description of data mining and its potential dangers and a short section on potential institutional safeguards to these problems set the stage for this exploration.
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