Discrete choice models with multiplicative error terms

Fosgerau, Mogens; Bierlaire, Michel

doi:10.1016/j.trb.2008.10.004

Cited by 96 publications

(76 citation statements)

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“…There are several models which allow for this, including the covariance heterogeneity model (DeShazo and Fermo 2002), the error components model (Hensher et al 2008;Savage and Waldman 2008), modeling Gumbel variance directly by using socio-economic characteristics (Scarpa et al 2003), the heteroskedastic extreme value model (Salisbury and Feinberg 2010), multiplicative errors model (Fosgerau and Bierlaire 2009) and, perhaps most notably, the heteroskedastic MNL model (e.g. Hensher et al 1998;Dellaert et al 1999;Swait and Adamowicz 2001;Caussade and Ortúzar 2005).…”

Section: Methods For Accounting For Information-related Effects On Prmentioning

confidence: 99%

Controlling for the Effects of Information in a Public Goods Discrete Choice Model

Czajkowski

Hanley

LaRiviere

2014

Environ Resource Econ

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This paper develops a reduced form method of controlling for differences in information sets of subjects in public good discrete choice models, using stated preference data. The main contribution of our method comes from accounting for the effect of information provided during a survey on the mean and the variance of individual-specific scale parameters. In this way we incorporate both scale heterogeneity as well as observed and unobserved preference heterogeneity to investigate differences across and within information treatments. Our approach will also be useful to researchers who want to combine stated preference data sets while controlling for scale differences. We illustrate our approach using the data from a discrete choice experiment study of a biodiversity conservation program and find that the mean of individual-specific scale parameters and its variance in the sample is sensitive to the information set provided to the respondents.

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Section: Methods For Accounting For Information-related Effects On Prmentioning

confidence: 99%

Controlling for the Effects of Information in a Public Goods Discrete Choice Model

Czajkowski

Hanley

LaRiviere

2014

Environ Resource Econ

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“…Because these assumptions are related to each other to some extents, relaxing one assumption may affect the others (see e.g. Castillo et al, 2008;Fosgerau and Bierlaire, 2009). …”

Section: Introductionmentioning

confidence: 99%

“…Further they demonstrate that if the random variables for different alternatives follow the Weibull distribution, then a closed-form expression for the choice probabilities can be obtained from the utility-maximizing behavior. Furthermore, Fosgerau and Bierlaire (2009) show that the assumption of the Weibull distribution is associated with the discrete choice model having multiplicative error terms, and the log-transformation links the multiplicative model to the additive model for which the Gumbel distribution is assumed. In addition, Castillo et al (2008) and Fosgerau and Bierlaire (2009) find that the Weibull-distribution-based model allows random variables with heteroscedastic variances.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, Fosgerau and Bierlaire (2009) show that the assumption of the Weibull distribution is associated with the discrete choice model having multiplicative error terms, and the log-transformation links the multiplicative model to the additive model for which the Gumbel distribution is assumed. In addition, Castillo et al (2008) and Fosgerau and Bierlaire (2009) find that the Weibull-distribution-based model allows random variables with heteroscedastic variances. As a consequence, performing the log-transformation can achieve two goals simultaneously: on the one hand it stabilizes variances, and on the other it specifies the Weibull distribution as the underlying distribution instead of the conventional Gumbel distribution.…”

Section: Introductionmentioning

confidence: 99%

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The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

2011

Transportation Research Part B: Methodological

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• ABSTRACTThe multinomial logit model in discrete choice analysis is widely used in transport research.It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenience. This can be restrictive in many other scenarios in practice. In this paper we show that the assumption of the Gumbel distribution can be substantially relaxed to include a large class of distributions that is stable with respect to the minimum operation. The distributions in the class allow heteroscedastic variances. We then seek a transformation that stabilizes the heteroscedastic variances. We show that this leads to a semiparametric choice model which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function. This sensitivity function reflects the degree of travelers" sensitivity to the changes in the combined travel cost. The estimation of the semiparametric choice model is also investigated and empirical studies are used to illustrate the developed method.

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“…A more technical conclusion from their work is that models with multiplicative error terms were better than the standard models with additive error terms (Fosgerau and Bierlaire, 2009). There were empirical identification problems for the models with exponential utility rates, which points to a need for developing stated preference designs to strengthen identification of the slope model.…”

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confidence: 96%

The valuation of travel-time variability

Fosgerau

2017

ITF Roundtable Reports

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Discrete choice models with multiplicative error terms

Cited by 96 publications

References 10 publications

Controlling for the Effects of Information in a Public Goods Discrete Choice Model

Controlling for the Effects of Information in a Public Goods Discrete Choice Model

The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

The valuation of travel-time variability

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