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A RANK‐ORDERED LOGIT MODEL WITH UNOBSERVED HETEROGENEITY IN RANKING CAPABILITIES
Author(s) -
Fok Dennis,
Paap Richard,
Van Dijk Bram
Publication year - 2012
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1223
Subject(s) - ranking (information retrieval) , rank (graph theory) , econometrics , logit , set (abstract data type) , mixed logit , ordered logit , logistic regression , task (project management) , statistics , computer science , economics , mathematics , information retrieval , management , combinatorics , programming language
Abstract To study preferences, respondents to a survey are usually asked to select their most preferred option from a set. Preferences can be estimated more efficiently if respondents are asked to rank all alternatives. When some respondents are unable to perform the ranking task, using the complete ranking may lead to a substantial bias. We introduce a model which endogenously describes the ranking capabilities of individuals. Estimated preferences based on this model are more efficient when at least some individuals are able to rank more than one item, and they do not suffer from biases due to ranking inabilities of respondents. Copyright © 2010 John Wiley & Sons, Ltd.