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The geometry of distributional preferences and a non-parametric identification approach: The Equality Equivalence Test.

Kerschbamer R - Eur Econ Rev (2015)

Bottom Line: This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context.It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants.As a by-product the test yields a two-dimensional index of preference intensity.

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Affiliation: Department of Economics, University of Innsbruck, Universitätsstrasse 15, A-6020 Innsbruck, Austria.

ABSTRACT

This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity.

No MeSH data available.


The geometry of the Equality Equivalence Test.
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f0020: The geometry of the Equality Equivalence Test.

Mentions: g is a “gap” variable characterizing the vertical distance between (e, e) and the two horizontal lines in Fig. 3 (see Fig. 4); in order to avoid zero or negative monetary payoffs we restrict g to values strictly smaller than e;


The geometry of distributional preferences and a non-parametric identification approach: The Equality Equivalence Test.

Kerschbamer R - Eur Econ Rev (2015)

The geometry of the Equality Equivalence Test.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4459445&req=5

f0020: The geometry of the Equality Equivalence Test.
Mentions: g is a “gap” variable characterizing the vertical distance between (e, e) and the two horizontal lines in Fig. 3 (see Fig. 4); in order to avoid zero or negative monetary payoffs we restrict g to values strictly smaller than e;

Bottom Line: This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context.It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants.As a by-product the test yields a two-dimensional index of preference intensity.

View Article: PubMed Central - PubMed

Affiliation: Department of Economics, University of Innsbruck, Universitätsstrasse 15, A-6020 Innsbruck, Austria.

ABSTRACT

This paper proposes a geometric delineation of distributional preference types and a non-parametric approach for their identification in a two-person context. It starts with a small set of assumptions on preferences and shows that this set (i) naturally results in a taxonomy of distributional archetypes that nests all empirically relevant types considered in previous work; and (ii) gives rise to a clean experimental identification procedure - the Equality Equivalence Test - that discriminates between archetypes according to core features of preferences rather than properties of specific modeling variants. As a by-product the test yields a two-dimensional index of preference intensity.

No MeSH data available.