Sharp for SARP: Nonparametric Bounds on Counterfactual Demands
推导了反事实需求和希克斯补偿变差与等价变差的锐化非参数边界，这些边界在强显示偏好公理下是锐化的，并通过易于实现的算法计算福利度量。
Sharp nonparametric bounds are derived for counterfactual demands and Hicksian compensating and equivalent variations. These “i-bounds” refine and extend earlier results of Blundell, Browning, and Crawford (2008). We show that their bounds are sharp under the Weak Axiom of Revealed Preference (WARP) since they do not require transitivity. The new bounds are sharp under the Strong Axiom of Revealed Preference (SARP). By requiring transitivity they can be used to bound welfare measures. The new bounds on welfare measures are shown to be operationalized through algorithms that are easy to implement.