Correcting for endogeneity due to omitted attitudes: Empirical assessment of a modified MIS method using RP mode choice data
Anna Fernández-Antolín,
Cristian Guevara,
Matthieu de Lapparent and
Michel Bierlaire
Journal of choice modelling, 2016, vol. 20, issue C, 1-15
Abstract:
In this paper we extend the Multiple Indicator Solution (MIS) so that it can also be used to account for endogeneity when there are interactions between observed and unobserved factors in the specification of the utility function. We develop the theoretical derivation and illustrate it with a revealed preference case study of mode choice. Policy indicators such as time elasticity and value of time are discussed. The results are compared with a logit model and with an Integrated Choice and Latent Variable (ICLV) model. Results show that endogeneity is present in the case study and that the proposed variation of the MIS method is practical and able to account for it. Our proposed method can be seen as a starting point for the practical detection and treatment of this type of endogeneity without the drawbacks of multifold integration.
Keywords: Discrete choice models; Mode choice; Value of time; Endogeneity; Multiple indicator solution; Latent variable; Revealed preference (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1755534516300288
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:20:y:2016:i:c:p:1-15
DOI: 10.1016/j.jocm.2016.09.001
Access Statistics for this article
Journal of choice modelling is currently edited by S. Hess and J.M. Rose
More articles in Journal of choice modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().