Posterior Average Effects
Posterior Average Effects
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后验平均效应
DOI：
10.1080/07350015.2021.1984928
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发表时间：
2019
影响因子：
3
通讯作者：
S. Bonhomme
中科院分区：
文献类型：
--
作者：
M. Weidner;S. Bonhomme
摘要 经济学家通常对估计不可观测分布的平均值感兴趣，例如个体固定效应的矩或离散选择模型中的平均部分效应。对于这些量，我们提出并研究后验平均效应（PAE），本着经验贝叶斯和收缩方法的精神，根据样本计算平均值。虽然收缩对于预测的有用性已广为人知，但目前缺乏后验条件来估计总体平均值的合理性。我们证明，在各种形式的不可观参数分布错误指定下，PAE 具有最小的最坏情况指定误差。此外，我们引入了后验条件信息量的度量，它量化了 PAE 相对于基于参数模型的估计器的最坏情况规范误差。作为说明，我们报告了 PAE 对美国邻里效应分布的估计，以及收入动态模型中永久和暂时成分的估计。
Abstract Economists are often interested in estimating averages with respect to distributions of unobservables, such as moments of individual fixed-effects, or average partial effects in discrete choice models. For such quantities, we propose and study posterior average effects (PAE), where the average is computed conditional on the sample, in the spirit of empirical Bayes and shrinkage methods. While the usefulness of shrinkage for prediction is well-understood, a justification of posterior conditioning to estimate population averages is currently lacking. We show that PAE have minimum worst-case specification error under various forms of misspecification of the parametric distribution of unobservables. In addition, we introduce a measure of informativeness of the posterior conditioning, which quantifies the worst-case specification error of PAE relative to parametric model-based estimators. As illustrations, we report PAE estimates of distributions of neighborhood effects in the U.S., and of permanent and transitory components in a model of income dynamics.
反事实敏感性和稳健性
影响因子：
6.1
作者：
Christensen, Timothy;Connault, Benjamin
通讯作者：
Connault, Benjamin