Willingness-to-Pay Estimation with Mixed Logit Models: Some New Evidence

By M. Sillano, Juan de Dios Ortúzar. 2005

351 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Decision Sciences, Transportation

View Paper: https://www.semanticscholar.org/paper/a5bdbd6e741346f72ca5c2855b0300dc5fe6f4d6

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Citations in the corpus

Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model (2013, methodology): “…these reasons, we propose to go beyond classical methods by introducing Bayesian techniques (for different dimensions of the Bayesian approach applied to transportation demand modelling, consult Ben-Akiva et al. (1999), Sillano and Ortúzar (2005), Santosoand Tsunokawa (2010), Lee et al. (2010)).”
Willingness to pay for environmental attributes of non-food agricultural products: a real choice experiment (2013, background): “…of symmetry of the hypothesised distribution allows the decision-maker to display negative WTP. Discussion about the choice of an appropriate distribution and its impact on the estimated WTP is still in progress in the literature (see for example Hess et al., 2004, 2008, Sillano and Ortuzar, 2005).”
Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models (2010, methodology): “Following the methodology proposed by Williams and Ortúzar (10), a collection of 60 data sets were simulated from N = 50,000 pseudoobserved individuals, who behaved according to a determined choice process.”
Estimating the willingness-to-pay and value of risk reduction for car occupants in the road environment (2009, methodology): “Recent new and innovative methods have been developed to allocate the attribute levels to the design matrix that do not rely on orthogonality as a design principle (see e.g., Bliemer and Rose 2006; Carlsson and Martinsson 2002; Ferrini and Scarpa 2007; Huber and Zwerina 1996; Kanninen 2002; Kessels ”
Keep it local and fish-friendly: Social acceptance of hydropower projects in Switzerland (2017, methodology): “The advantage of the Bayesian approach over the classical procedure is its ease of implementation - due to the use of Gibbssampling and the Metropolis-Hastings algorithm it does not involve a maximization processes [53].”