AI 中文总结
本文提出混频随机前沿模型，通过后向拟合算法的混合方法估计，解决随机前沿模型正偏度问题，将其用于分析菲律宾电力合作社数据中企业效率与极端天气的关联，为评估气候变化引发的微观可持续性问题提供方法。
AI 中文摘要
本文提出一种策略，将低频率生产函数指标编制的随机前沿模型中，纳入低频率的无效率的高频决定因素。此外，通过对无效率方程假设逻辑函数、不假设无效率项更复杂分布的方式，解决了随机前沿模型中正偏度的问题。高频决定因素以非参数函数形式纳入无效率方程，该模型采用后向拟合算法中的混合方法估计，而非通常存在收敛问题且易受分布假设影响的最大似然估计（MLE）。随后，该模型被用于表征菲律宾电力合作社数据中企业效率与极端天气事件的关联，为评估气候变化引发的极端天气导致的微观层面可持续性问题提供了方法。
英文摘要
A strategy for incorporating higher-frequency determinants of inefficiency into the stochastic frontier models with production function indicators compiled at lower frequencies is proposed. Furthermore, the problem with positive skewness in stochastic frontier models is resolved by postulating a logistic function for the inefficiency equation and refraining from assuming a more complicated distribution of the inefficiency term. High frequency determinants are incorporated into the inefficiency equation in a nonparametric function. The model is estimated with hybrid methods in a backfitting algorithm instead of maximum likelihood estimation (MLE), which typically struggles with convergence issues and is vulnerable to distributional assumptions. The model is then used to characterize the linkage between firm efficiency and extreme weather events using data from electric cooperatives in the Philippines. This provides an approach to assess micro level sustainability issues from extreme weather arising events from climate change.
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