Specifying and testing econometric models for rank-ordered data

Hausman, Jerry A.; Ruud, Paul A.

doi:10.1016/0304-4076(87)90068-6

Cited by 255 publications

(186 citation statements)

References 10 publications

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“…(Torrance et al 1996;Feeny et al 2002;McCabe et al, 2004b The analyses assume that the information content of the rank is unaffected by the order of the rank or indeed the number of states to be ranked. Hausman and Ruud (1987) have hypothesised that respondents may take more care with the initial ranking exercises than the later ones. Thus the risk of a ranking being incorrect would be systematically related to a health state's position in the rank; i.e.…”

Section: Discussionmentioning

confidence: 99%

Using rank data to estimate health state utility models

McCabe

Brazier

Gilks

et al. 2006

Journal of Health Economics

110

106

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In this paper we report the estimation of conditional logistic regression models for the Health Utilities Index Mark 2 and the SF-6D, using ordinal preference data. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations.For both the HUI2 and the SF-6D, the models estimated using ordinal data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that ordinal data have the potential to provide useful insights into community health state preferences.However important questions remain.  AcknowledgementThe authors wish to acknowledge the support and encouragement of their co-workers on the SF-6D and HUI2 valuation projects. In addition, George Torrance from McMaster University and Joshua Salomon from Harvard University provided valuable comments on the work as it progressed. We are grateful to the UK Medical Research Council, who funded the HUI2 valuation project and to GlaxoWellcome, who funded the SF-6D valuation project. All errors and omissions remain the authors' responsibility.3

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Section: Discussionmentioning

confidence: 99%

Using rank data to estimate health state utility models

McCabe

Brazier

Gilks

et al. 2006

Journal of Health Economics

110

106

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“…If one assumes k to be the same for the whole sample, this last term can be ignored in the estimation as it becomes a constant in the log likelihood, see Chapman and Staelin (1982) and Hausman and Ruud (1987). These two papers propose estimating different ROL models, each using a different number of ranks, that is a different value for k. Then they have different methods of choosing a model from this set.…”

Section: Ranking Abilitymentioning

confidence: 99%

“…The 1,000 individuals are divided in one of the three segments according to the mixing proportions. Chapman and Staelin (1982) and Hausman and Ruud (1987).…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

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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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“…However, such predictions presuppose that the constants m a are zero, that is, that the included attributes fully explain the choices. The model is used for both first choices (e.g., McFadden, 1973) and rankings (e.g., Hausman & Ruud, 1987). The polytomous logistic regression model, a standard model for first choices in for instance biostatistics (e.g., Hosmer & Lemeshow, 1989), results as the special case where x a i b = 0 in (4).…”

Section: Fixed Effects Onlymentioning

confidence: 99%

Multilevel logistic regression for polytomous data and rankings

2003

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We propose a unifying framework for multilevel modeling of polytomous data and rankings, accommodating dependence induced by factor and/or random coefficient structures at different levels. The framework subsumes a wide range of models proposed in disparate methodological literatures. Partial and tied rankings, alternative specific explanatory variables and alternative sets varying across units are handled. The problem of identification is addressed. We develop an estimation and prediction methodology for the model framework which is implemented in the generally available gllamm software. The methodology is applied to party choice and rankings from the 1987-1992 panel of the British Election Study. Three levels are considered: elections, voters and constituencies.

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Specifying and testing econometric models for rank-ordered data

Cited by 255 publications

References 10 publications

Using rank data to estimate health state utility models

Using rank data to estimate health state utility models

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

Multilevel logistic regression for polytomous data and rankings

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