Semiparametric Estimation of the Binary Choice Model for Contingent Valuation
在不假设随机项分布形式的前提下，采用无分布最大似然法估计二元选择模型，用于推导条件价值函数，并通过蒙特卡洛模拟与实证数据验证其有效性。
This paper is concerned with the estimation of the binary choice model without imposing any parametric structure on the distribution of the stochastic term. We adapt the distribution-free maximum likelihood method developed by Cosslett (1983) for deriving the contingent value function with respect to observable exogenous variables. We present Monte Carlo comparisons with the probit estimates and discuss the asymptotic consistency and relative efficiency of the approach. Data from a forest environment valuation survey are used for empirical estimations.