2018
DOI: 10.3982/ecta14478
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Nonparametric Analysis of Random Utility Models

Abstract: This paper develops and implements a nonparametric test of random utility models. The motivating application is to test the null hypothesis that a sample of cross‐sectional demand distributions was generated by a population of rational consumers. We test a necessary and sufficient condition for this that does not restrict unobserved heterogeneity or the number of goods. We also propose and implement a control function approach to account for endogenous expenditure. An econometric result of independent interest… Show more

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Cited by 97 publications
(116 citation statements)
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“… This general point about partial identification analysis has been appreciated (sometimes implicitly) by many previous authors, including Honoré and Tamer (), Manski (), Molinari (), Chiburis (), Kitamura and Stoye (, ), Manski (), Freyberger and Horowitz (), and Lafférs (, ). In particular, Lafférs (, ) used a similar computational strategy as in this paper, but for a static potential outcomes model; see also the subsequent work by Demuynck ().…”
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confidence: 72%
“… This general point about partial identification analysis has been appreciated (sometimes implicitly) by many previous authors, including Honoré and Tamer (), Manski (), Molinari (), Chiburis (), Kitamura and Stoye (, ), Manski (), Freyberger and Horowitz (), and Lafférs (, ). In particular, Lafférs (, ) used a similar computational strategy as in this paper, but for a static potential outcomes model; see also the subsequent work by Demuynck ().…”
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confidence: 72%
“…Tests for this hypothesis have been proposed both for the case of nonparametric as well as semiparametric partially identified models. I refer to Santos (2012) for specification tests in a partially identified nonparametric instrumental variable model; to Kitamura and Stoye (2018) for a nonparametric test in random utility models that checks whether a repeated cross section of demand data might have been generated by a population of rational consumers (thereby testing for the Axiom of Revealed Stochastic Preference); and to Guggenberger, Hahn, and Kim (2008) and Bontemps, Magnac, and Maurin (2012) for specification tests in linear moment (in)equality models.…”
Section: Misspecification In Partially Identified Modelsmentioning
confidence: 99%
“…Axiomatic analysis helps shed light on which behavior (e.g., the “attraction effect”) this model rules out, as well as which behavior (e.g., the restrictive “independence of irrelevant alternatives” assumption) is implied only by specific parametric versions of random utility but not by the general model . Moreover, some axioms have inspired empirical tests of the model (e.g., Hausman and McFadden (), Kitamura and Stoye ()).…”
Section: Introductionmentioning
confidence: 99%
“… For example, Hausman and McFadden () developed a test of the IIA axiom that characterizes the logit model. Likewise, Kitamura and Stoye () developed axiom‐based tests of the static random utility model. …”
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confidence: 99%