Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study

By Silvia Ferrini, Riccardo Scarpa. 2007

575 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management, Monitoring, Policy and Law, Marketing

View Paper: https://www.semanticscholar.org/paper/9f4deb40957942be83c0591d2ae8b8d05db59e88

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Citations in the corpus

Non-attendance to attributes in environmental choice analysis: a latent class specification (2011, methodology): “See Train (2003) or Hensher et al. (2005b) for a detailed econometric explanation of the difference between these responses and Campbell et al.”
Allowing for intra-respondent variations in coefficients estimated on repeated choice data (2009, methodology): “For a review of efficient SC design methods, see Bliemer and Rose (2006) or Ferrini and Scarpa (2007).”
Farmers' willingness to provide ecosystem services and effects of their spatial distribution (2013, background): “This error component reflects that there may be additional variance related to the two non-status quo alternatives, because it is cognitively more demanding for respondents to evaluate two complex alternatives in each choice set as opposed to the status quo (Ferrini and Scarpa, 2007; Greene and Hens”
Construction of experimental designs for mixed logit models allowing for correlation across choice observations (2010, methodology): “(2007), Ferrini and Scarpa (2007), and Sándor and Wedel (2002, 2005), research into the generation of SC experiments have largely assumed that data collected using the design will be limited in analysis to the use of the simple MNL model. The MNL model remains versatile and resilient many years afte”
Efficient stated choice experiments for estimating nested logit models (2009, methodology): “To date, most research on efficient designs have assumed an MNL model form (see Ferrini and Scarpa, 2007 and Sándor and Wedel, 2002, 2005 for the sole exceptions).”
We want to sort! – assessing households’ preferences for sorting waste (2014, unknown): “Our study used d-efficient Bayesian design (Ferrini & Scarpa 2007).”
Social Norms, Morals and Self-interest as Determinants of Pro-environment Behaviours: The Case of Household Recycling (2014, methodology): “The design was optimized for median D-efficiency of a multinomial logit model using Bayesian priors (Ferrini and Scarpa 2007) and all prior estimates were assumed to be normally distributed, with their means derived from the MNL model Fig.”
Spatial Heterogeneity of Willingness to Pay for Forest Management (2017, methodology): “The experimental design was optimized for Bayesian (median) d-efficiency of the MXL model (Sándor and Wedel 2001; Ferrini and Scarpa 2007; Bliemer et al. 2008; Scarpa and Rose 2008).”
Economic valuation of cultural heritage sites: A choice modeling approach (2010, methodology): “A D-efficient design was used to maximize the efficiency of parameter estimates by providing minimum values of all the elements in the variance–covariance matrix (Bliemer & Rose, 2005; Ferrini & Scarpa, 2007; Kuhfeld, Tobias, & Garratt, 1994; Table 1 Attributes and their levels for OPH.”
Benefit Estimates for Landscape Improvements: Sequential Bayesian Design and Respondents’ Rationality in a Choice Experiment (2005, methodology): “Hence, efficiency gains were sought and achieved by adopting for the first time in the public good valuation literature, a Bayesian experimental design using the algorithm proposed by Sa ´ ndor and Wedel (2001) with a variant that involved sequential updating, as suggested by Ferrini and Scarpa (20”
Working Paper: The Fragmentation of Cities and its Decline: Global Evidence of Sprawl, 1990-2000 (2009, unknown): “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 ”