Implications of Thresholds in Discrete Choice Modelling

Victor Cantillo and Juan de Dios Ortúzar

Transport Reviews, 2005, vol. 26, issue 6, 667-691

Abstract: Individual choices are affected by complex factors and the challenge consists of how to incorporate these factors in order to improve the realism of the modelling work. The presence of limits, cut‐offs or thresholds in the perception and appraisal of both attributes and alternatives is part of the complexity inherent to choice‐making behaviour. The paper considers the existence of thresholds in three contexts: inertia (habit or reluctance to change), minimum perceptible changes in attribute values, and as a mechanism for accepting or rejecting alternatives. It discusses the more relevant approaches in modelling these types of thresholds and analyses their implications in model estimation and forecasting using both synthetic and real databanks. It is clear from the analysis that if thresholds exist but are not considered, the estimated models will be biased and may produce significant errors in prediction. Fortunately, there are practical methods to attack this problem and some are demonstrated.

Date: 2005
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/01441640500487275 (text/html)
Access to full text is restricted to subscribers.

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:taf:transr:v:26:y:2005:i:6:p:667-691

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TTRV20

DOI: 10.1080/01441640500487275

Access Statistics for this article

Transport Reviews is currently edited by Professor David Banister and Moshe Givoni

More articles in Transport Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().