Predictive Power in Behavioral Welfare Economics
研究了当选择因行为偏差而不一致时，两种“无模型”方法在行为福利分析中的预测力，发现它们通常具有高预测力，从而减少了选择集中的模糊性。
Abstract When choices are inconsistent due to behavioral biases, there is a theoretical debate about whether the structure of a model is necessary for providing precise welfare guidance based on those choices. To address this question empirically, we use standard data sets from the lab and field to evaluate the predictive power of two “model-free” approaches to behavioral welfare analysis. We find they typically have high predictive power, which means there is little ambiguity about what should be selected from each choice set. We also identify properties of revealed preferences that help to explain the predictive power of these approaches.