A Theory of Dichotomous Valuation with Applications to Variable Selection

Xingwei Hu

MPRA Paper from University Library of Munich, Germany

Abstract: An econometric or statistical model may undergo a marginal gain when a new variable is admitted, and marginal loss if an existing variable is removed. The value of a variable to the model is quantified by its expected marginal gain and marginal loss. Under a prior belief that all candidate variables should be treated fairly, we derive a few formulas which evaluate the overall performance of each variable. One formula is identical to that for the Shapley value. However, it is not symmetric with respect to marginal gain and marginal loss; moreover, the Shapley value favors the latter. Thus we propose a unbiased solution. Two empirical studies are included: the first being a multi-criteria model selection for a dynamic panel regression; the second being an analysis of effect on hourly wage given by additional years of schooling.

Keywords: unbiased multivariate Shapley value; variable selection; marginal effect; endowment bias; model uncertainty (search for similar items in EconPapers)
JEL-codes: C11 C52 C57 C71 D81 (search for similar items in EconPapers)
Date: 2017-06-15
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/80457/1/MPRA_paper_80457.pdf original version (application/pdf)

Related works:
Journal Article: A theory of dichotomous valuation with applications to variable selection (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:80457

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().