Multiple herbicide use in cropland: A discrete continuous model for stated choice data

2021 ◽  
pp. 092520-0150R1
Author(s):  
Andrea Pellegrini ◽  
John Rose ◽  
Riccardo Scarpa
Keyword(s):  
Continuous Model ◽  
Stated Choice ◽  
Choice Data ◽  
Herbicide Use

Understanding suburban travel demand: Flexible modelling with revealed and stated choice data

2007 ◽  
Vol 41 (10) ◽  
pp. 899-912 ◽  
Author(s):  
Raquel Espino ◽  
Juan de Dios Ortúzar ◽  
Concepción Román
Keyword(s):  
Travel Demand ◽  
Stated Choice ◽  
Choice Data

Identifying a behaviourally relevant choice set from stated choice data

2014 ◽  
Vol 43 (2) ◽  
pp. 197-217 ◽  
Author(s):  
David A. Hensher ◽  
Chinh Ho
Keyword(s):  
Stated Choice ◽  
Choice Data ◽  
Choice Set

scholarly journals Non-trading, lexicographic and inconsistent behaviour in stated choice data

2010 ◽  
Vol 15 (7) ◽  
pp. 405-417 ◽  
Author(s):  
Stephane Hess ◽  
John M. Rose ◽  
John Polak
Keyword(s):  
Stated Choice ◽  
Choice Data

scholarly journals Congestion management in protected areas: accounting for respondents’ inattention and preference heterogeneity in stated choice data

2018 ◽  
Vol 46 (5) ◽  
pp. 834-861 ◽  
Author(s):  
Mara Thiene ◽  
Cristiano Franceschinis ◽  
Riccardo Scarpa
Keyword(s):  
Protected Areas ◽  
Latent Class ◽  
Congestion Management ◽  
Model Fit ◽  
Destination Choice ◽  
Preference Heterogeneity ◽  
Stated Choice ◽  
Random Parameters ◽  
Choice Data ◽  
Traditional Approaches

Abstract Congestion levels in protected areas can be predicted by destination choice models estimated from choice data. There is growing evidence of subjects’ inattention to attributes in choice experiments. We estimate an attribute non-attendance latent class–random parameters model (LC–RPL) that jointly handles inattention and preference heterogeneity. We use data from a choice experiment designed to elicit visitors’ preferences towards sustainable management of a protected area in the Italian Alps. Results show that the LC–RPL model produces improvements in model fit and reductions in the implied rate of inattention, as compared to traditional approaches. Implications of results for park management authorities are discussed.

scholarly journals randregret: A command for fitting random regret minimization models using Stata

2021 ◽  
Vol 21 (3) ◽  
pp. 626-658
Author(s):  
Álvaro A. Gutiérrez-Vargas ◽  
Michel Meulders ◽  
Martina Vandebroek
Keyword(s):  
Likelihood Ratio Tests ◽  
Model Specification ◽  
Stated Choice ◽  
Choice Data ◽  
Score Functions ◽  
Regret Minimization ◽  
Transportation Research ◽  
Analytical Expressions ◽  
Random Regret Minimization ◽  
Transportation Research Part

In this article, we describe the randregret command, which implements a variety of random regret minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181–196), the generalized RRM model introduced in Chorus (2014, Transportation Research, Part B 68: 224–238), and also the µRRM and pure RRM models, both introduced in van Cranenburgh, Guevara, and Chorus (2015, Transportation Research, Part A 74: 91–109). We illustrate the use of the randregret command by using stated choice data on route preferences. The command offers robust and cluster standarderror correction using analytical expressions of the score functions. It also offers likelihood-ratio tests that can be used to assess the relevance of a given model specification. Finally, users can obtain the predicted probabilities from each model by using the randregretpred command.

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