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Specification issues in a generalised random parameters attribute nonattendance model
Institution:1. Institute of Transport and Logistics Studies, The University of Sydney Business School, The University of Sydney, NSW 2006, Australia;1. Institute for Clinical Effectiveness and Health Policy, Buenos Aires, Argentina;2. Unidad de Análisis y Generación de Evidencias en Salud Pública, Instituto Nacional de la Salud, Lima, Perú;3. EuroQol Research Foundation, Rotterdam, The Netherlands;4. University of South Florida, Tampa, FL, USA;1. Department of Nutrition and Dietetics, Flinders University, Adelaide, Australia;2. Flinders Health Economics Group, Flinders University, Adelaide, Australia;3. Centre for Health Economics, Monash University, Melbourne, Australia;4. Department of Rehabilitation and Aged Care, Flinders University, Adelaide, Australia
Abstract:An extensive literature has recognised that when travel choices are made, only a subset of the attributes of the choice alternatives may be considered or attended to by each decision maker. Numerous econometric approaches have been employed to identify attribute nonattendance (ANA), with the most prevalent in the literature being an adaptation of the latent class model. However, the two latent class structures so far employed either incur a potentially very high parametric cost, or rely on an assumption that nonattendance is independent across all attributes. We present a generalised model that allows for an arbitrary degree of correlation of nonattendance across attributes. In the presented stated choice study investigating short haul flights, this generalised model outperforms the existing approaches. Like two recent papers, the model handles both ANA and preference heterogeneity by combining continuously distributed random parameters with latent classes. However, we present recommendations regarding a number of identification issues stemming from the combination of these two forms of random parameters not covered in those papers. Further, covariates can be introduced into our generalised model to allow insights to be gained into ANA behaviour. We investigate stated ANA as a covariate, and find inferred ANA rates to be more aligned with stated ANA responses than alternative methods.
Keywords:Attribute nonattendance  Random parameters attribute nonattendance model  Latent class model  Random parameters logit
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