On dichotomous choice contingent valuation data analysis: Semiparametric methods and Genetic Programming

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@Article{AlvarezDiaz2009,

• author = "Marcos {Alvarez Diaz} and Manuel Gonzalez Gomez and Angeles {Saavedra Gonzalez} and Jacobo {De Una Alvarez}",
• title = "On dichotomous choice contingent valuation data analysis: Semiparametric methods and Genetic Programming",
• journal = "Journal of Forest Economics",
• year = "2010",
• volume = "16",
• number = "2",
• pages = "145--156",
• month = apr,
• keywords = "genetic algorithms, genetic programming, Dichotomous choice contingent valuation, Genetic program, Parametric techniques, Proportional hazard model",
• ISSN = "1104-6899",
• DOI = "doi:10.1016/j.jfe.2009.02.002",
• broken = "http://www.sciencedirect.com/science/article/B7GJ5-4XY3F46-1/2/d98566d6ee97a4f7f2c2f1b9deb29bc1",
• size = "12 pages",
• abstract = "The aim of this paper is twofold. Firstly, we introduce a novel semi-parametric technique called Genetic Programming to estimate and explain the willingness to pay to maintain environmental conditions of a specific natural park in Spain. To the authors' knowledge, this is the first time in which Genetic Programming is employed in contingent valuation. Secondly, we investigate the existence of bias due to the functional rigidity of the traditional parametric techniques commonly employed in a contingent valuation problem. We applied standard parametric methods (logit and probit) and compared with results obtained using semi parametric methods (a proportional hazard model and a genetic program). The parametric and semiparametric methods give similar results in terms of the variables finally chosen in the model. Therefore, the results confirm the internal validity of our contingent valuation exercise.",
• notes = "2022 https://www.nowpublishers.com/JFE",

}

Genetic Programming entries for Marcos Alvarez-Diaz Manuel Gonzalez Gomez Maria Angeles Saavedra Gonzalez Jacobo De Una Alvarez

Citations