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EconStor
Research . 1998
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Nonparametric identification of discrete choice models

Authors: John K. Dagsvik;

Nonparametric identification of discrete choice models

Abstract

Abstracts: In this paper we give simple proofs of identification results in discrete choice models for the case where neither the derteministic part nor the distribution function of the random parts of the utility function is specified parametrically. The regularity conditions imposed are standard, but differ from conditions applied by other researchers, such as Matzkin (1992, 1993).

Country
Norway
Related Organizations
Keywords

Random utility models, Matematiske modeller, Matematisk statistikk, VDP::Social science: 200::Economics: 210, VDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412, ddc:330, Discrete choice, Nonparametric identification; Discrete choice; Random utility models, C14, C25, Nonparametric identification, jel: jel:C14, jel: jel:C25

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green