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

Stellenbosch Working Paper Series No. WP15/2006
 
Publication date: 2006
 
Author(s):
[protected email address] (Department of Economics, University of Stellenbosch)
 
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.

 
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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BER Weekly

13 September 2021
It was a data-heavy week in SA, with the 2021Q2 real GDP data indicating that the economy had better-than-expected recovery momentum in the first half of the year. While growth remained solid in the second quarter, both the survey and actual data released last week revealed the significant impact that the range of shocks at the start of the third quarter...

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