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

Abstract

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

Effects of providing total cost of ownership information on consumers’ intent to purchase a hybrid or plug-in electric vehicle

“…, N individuals. For individual i, the utility of alternative j is given by U i j (Fok et al, 2012). In the random utility framework, it is assumed that the researcher does not directly observe U i j .…”

“…r J i ). Then the probability to observe a particular ranking is written as (Borzekowski and Kiser, 2008;Fok et al, 2012;Lee and Yu, 2013) …”

The use of alternative preference elicitation methods in complex discrete choice experiments

“…The basic idea is to model all parameters in HROL as person-specific random parameters. In this paper, we describe the resulting model from the conventional perspective that rank heteroskedasticity arises as people are more certain about what they like more (see for example, Fok et al, 2012). Alternatively, Yoo (2012) motivates the use of the same model to account for stochastic misspecification of the microeconomic random utility function (McFadden, 1981).…”

A Latent Class Nested Logit Model for Rank-Ordered Data with Application to Cork Oak Reforestation

“…It is therefore important to consider developing econometric methods for rank-ordered data, to complement the continual effort to understand better and improve ranking survey designs (Caparrós, Oviedo and Campos, 2008;Chang, Lusk and Norwood, 2009;Scarpa et al, 2011;Akaich, Nayga, and Gil, 2013;Louviere, Flynn and Marley, 2015). The existing approach to analyzing rank-ordered data usually exploits extensions and variants of the exploded logit (Chapman and Staelin, 1982), both within (Chang, Lusk and Norwood, 2009;Scarpa et al, 2011;Resano, Sanjuan and Albisu, 2012;Othman and Rahajeng, 2013;Varela et al, 2014) and outside (Fok, Paap and Van Dijk, 2012;Yoo and Doiron, 2013) the environmental valuation literature. Our strategy adds to the empirical practitioner's toolkit an approach building on the nested rank-ordered logit of Dagsvik and Liu (2009) that allows for more plausible substitution patterns.…”