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前沿处配置：前沿结构函数识别与平均偏离界限AI 解读

This paper analyzes a model in which an outcome equals a frontier function of inputs minus a nonnegative unobserved deviation. The inputs may be endogenous (statistically dependent on the deviation). If zero lies in the support of the deviation given the inputs -- an assumption we term assignment at the frontier -- then the frontier is identified by the supremum of the outcome given those inputs, obviating the need for instruments. We then consider estimation with random…

非平衡两层与三层模型中中心矩的无偏估计AI 解读

This paper derives closed-form unbiased estimators of central moments in multilevel random-effects models with unbalanced group sizes. In a two-level model, we provide unbiased estimators for the second, third, and fourth central moments under both group-level and observation-level averaging. In a three-level model, we provide unbiased estimators for the second and third central moments.