An Exploration of Regression-Based Data Mining Techniques Using Super Computation

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An Exploration of Regression-Based Data Mining Techniques Using Super Computation

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Antony Davies () (Department of Economics Duquesne University The Mercatus Center George Mason University)

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Antony Davies

Abstract

Regression analysis is intended to be used when the researcher seeks to test a given hypothesis against a data set. Unfortunately, in many applications it is either not possible to specify a hypothesis, typically because the research is in a very early stage, or it is not desirable to form a hypothesis, typically because the number of potential explanatory variables is very large. In these cases, researchers have resorted either to overt data mining techniques such as stepwise regression, or covert data mining techniques such as running variations on regression models prior to running the final model (also known as “data peeking”). While data mining side-steps the need to form a hypothesis, it is highly susceptible to generating spurious results. This paper draws on the known properties of OLS estimators in the presence of omitted and extraneous variable models to propose a procedure for data mining that attempts to distinguish between parameter estimates that are significant due to an underlying structural relationship and those that are significant due to random chance.

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Paper provided by The George Washinton University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2008-8.

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Length: 36 pages
Date of creation: Aug 2008
Date of revision:
Handle: RePEc:gwc:wpaper:2008-8

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Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

This paper has been announced in the following NEP Reports:

NEP-ALL-2009-06-03 (All new papers)

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This page was last updated on 2009-11-14.

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