Bias caused by omitted variables in random regret choice models: Formal and empirical analysis of orthogonal design data

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Abstract

The aim of this study is to explore the bias caused by omitted variables in both random utility and random regret models. Since the behavioural underpinnings of these models differ, the bias is also expected to differ. We focus our analysis on stated choice data, based on orthogonal fractional factorial experimental designs, because the endogeneity, defined as a correlation between selected and omitted explanatory variables is a nonissue in such data. We will show that while the bias caused by omitted variables in the random utility model can be simply neutralised by using alternative specific constants, it still exists in the random regret model because the bias in this type of choice models depends on the attribute dominance structure in the data and thus is not constant.

Original languageEnglish
Title of host publicationProceedings of the 21st International Conference of Hong Kong Society for Transportation Studies, HKSTS 2016 - Smart Transportation
EditorsA.W.G. Wong, S.H.F. Wong, G.L.M. Leung
Place of PublicationHong Kong
PublisherHong Kong Society for Transportation Studies
Pages261-267
Number of pages7
ISBN (Electronic)9789881581457
Publication statusPublished - 2016
Event21st International Conference of Hong Kong Society for Transportation Studies (HKSTS 2016) - Hong Kong, Hong Kong
Duration: 10 Dec 201612 Dec 2016
http://www.hksts.org/conf16.htm

Conference

Conference21st International Conference of Hong Kong Society for Transportation Studies (HKSTS 2016)
Abbreviated titleHKSTS
CountryHong Kong
CityHong Kong
Period10/12/1612/12/16
OtherSmart Transportation
Internet address

Keywords

  • Omitted variables
  • Regret minimization
  • Utility maximization

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