The structure of random utility models

Manski, Charles F.

doi:10.1007/bf00133443

Cited by 1,308 publications

(661 citation statements)

References 13 publications

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“…We use the random utility framework, see Manski (1977), to represent the preferences of individuals. The random utilities for individual i are a set of latent variables U i1 , .…”

Section: Rank-ordered Logit Modelmentioning

confidence: 99%

A Rank‐ordered Logit Model With Unobserved Heterogeneity in Ranking Capabilities

Fok

Paap

Dijk

2010

J of Applied Econometrics

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In this paper we consider the situation where one wants to study the preferences of individuals over a discrete choice set through a survey. In the classical setup respondents are asked to select their most preferred option out of a (selected) set of alternatives. It is well known that, in theory, more information can be obtained if respondents are asked to rank the set of alternatives instead. In statistical terms, the preferences can then be estimated more efficiently. However, when individuals are unable to perform (part of) this ranking task, using the complete ranking may lead to a substantial bias in parameter estimates. In practice, one usually opts to only use a part of the reported ranking.In this paper we introduce a latent-class rank-ordered logit model in which we use latent segments to endogenously identify the ranking capabilities of individuals. Each segment corresponds to a different assumption on the ranking capability. Using simulations and an empirical application, we show that using this model for parameter estimation results in a clear efficiency gain over a multinomial logit model in case some individuals are able to rank. At the same time it does not suffer from biases due to ranking inabilities of some of the respondents.JEL Classification: C25, C52

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“…We use the random utility framework, see Manski (1977), to represent the preferences of individuals. The random utilities for individual i are a set of latent variables U i1 , .…”

Section: Rank-ordered Logit Modelmentioning

confidence: 99%

A Rank‐ordered Logit Model With Unobserved Heterogeneity in Ranking Capabilities

Fok

Paap

Dijk

2010

J of Applied Econometrics

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show abstract

“…The analytical framework is the same for both. It is assumed utility is derived from the underlying characteristics or attributes (Lancaster 1966) and typically on the Random Utility Model developed by McFadden (1973) and by Manski (1977), under which utility has a systematic and a random component. The random component may result from unobserved or unobservable attributes, unobserved taste variations, measurement errors or specification errors (Ben-Akiva & Lerman, 1985).…”

Section: B1 Discrete Choice Model Theorymentioning

confidence: 99%

What sort of Brexit do the British people want? A proof-of-concept study using stated preference discrete choice experiments -- Technical addendum

Rohr¹,

Pollitt²,

Howarth³

et al. 2017

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“…Manski (1977). With some behavourial assumptions on how choices are made, a multinomial logit model arises which can then be fitted to observed data.…”

Section: Introductionmentioning

confidence: 99%

Some aspects of random utility, extreme value theory and multinomial logit models

Andersson

Ubøe

2011

Stochastics

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The structure of random utility models

Cited by 1,308 publications

References 13 publications

A Rank‐ordered Logit Model With Unobserved Heterogeneity in Ranking Capabilities

A Rank‐ordered Logit Model With Unobserved Heterogeneity in Ranking Capabilities

What sort of Brexit do the British people want? A proof-of-concept study using stated preference discrete choice experiments -- Technical addendum

Some aspects of random utility, extreme value theory and multinomial logit models

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