This study applies an information theoretic econometric approach in the form of a new maximum likelihood-minimum power divergence (ML-MPD) semi-parametric binary response estimator to analyze dichotomous contingent valuation data. The ML-MPD method estimates the underlying behavioral decision process leading to a person's willingness to pay for river recreation site attributes. Empirical choice probabilities, willingness to pay measures for recreation site attributes, and marginal effects of changes in some explanatory variables are estimated. For comparison purposes, a Logit model is also implemented. A Wald test of the symmetric logistic distribution underlying the Logit model is rejected at the 0.01 level in favor of the ML-MPD distribution model. Our results also demonstrate the potential for substantially overstating the precision of the estimates and associated inferences when the imposition of unknown structural information is not accounted explicitly for in the model. The ML-MPD model provides more intuitively reasonable and defensible results regarding the valuation of river recreation than the Logit model.

这项研究以新的最大似然-最小乘方散度（ML-MPD）半参数二元响应估计器的形式应用信息理论计量经济学方法来分析二分类的或有估值数据。ML-MPD方法估计潜在的行为决策过程，从而导致人们愿意为河流休闲场所的属性付费。估计经验选择概率，为娱乐场所属性支付度量的意愿以及某些解释变量变化的边际效应。为了比较，还实现了Logit模型。Logit模型背后的对称逻辑分布的Wald检验在0.01级别被拒绝，有利于ML-MPD分布模型。我们的结果还表明，当模型中未明确考虑施加未知结构信息时，可能会严重高估估计值和相关推论的准确性。与Logit模型相比，ML-MPD模型提供了关于河流休闲评估的更直观，合理和可辩护的结果。