The incompatibility of achieving a fully specified linear model with assuming that residual dependent-variable variance is random

Merton Krause ()

Quality & Quantity: International Journal of Methodology, 2013, vol. 47, issue 6, 3204 pages

Abstract: The residual dependent-variable variance in experiments is not “random error”, as it is often assumed to be, but merely “unaccounted for variance”, because what is random is inexplicable in terms of any possible set of independent-variables and this is something that ultimately is only empirically determinable. So, if there is any unaccounted for dependent-variable variance, an experiment’s set of independent-variables is certainly under-specified and perhaps mis-specified because of the confounding of variables included in this set by causally relevant variables not included in the set. Thus, the proper first empirical test of any linear model is whether it leaves any residual dependent-variable variance, and if it does then none of its independent variables can yet logically justifiably be claimed to predict or causally explain any of the dependent-variable variance whatsoever. Copyright Springer Science+Business Media B.V. 2013

Keywords: Linear model; Residual dependent-variable variance; Random error; External confounding; Specification errors; Model testing (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11135-012-9712-5

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