Modeling Elicitation effects in contingent valuation studies: a Monte Carlo Analysis of the bivariate approach
Modeling Elicitation effects in contingent valuation studies: a Monte Carlo Analysis of the bivariate approach
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条件估值研究中的启发效应建模：双变量方法的蒙特卡罗分析
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
E. Strazzera
中科院分区：
文献类型：
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作者：
Margarita Genius;E. Strazzera
蒙特卡洛分析进行评估的双变量建模方法的有效性，检测和纠正不同形式的启发效应在双约束条件估值数据。替代单变量和双变量模型应用于几个模拟数据集，每个模拟数据集的特点是一个特定的启发效应，并使用标准选择标准评估其性能。双变量模型包括标准的双变量Probit模型，以及基于Copula方法进行多变量建模的替代规范，该规范在联合分布的正态性假设不受数据支持的情况下非常有用。据发现，双变量的方法可以有效地纠正启发效应，同时保持足够的效率水平，在估计的参数的兴趣。
A Monte Carlo analysis is conducted to assess the validity of the bivariate modeling approach for detection and correction of different forms of elicitation effects in Double Bound Contingent Valuation data. Alternative univariate and bivariate models are applied to several simulated data sets, each one characterized by a specific elicitation effect, and their performance is assessed using standard selection criteria. The bivariate models include the standard Bivariate Probit model, and an alternative specification, based on the Copula approach to multivariate modeling, which is shown to be useful in cases where the hypothesis of normality of the joint distribution is not supported by the data. It is found that the bivariate approach can effectively correct elicitation effects while maintaining an adequate level of efficiency in the estimation of the parameters of interest.