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摘要翻译：
提出了一种基于最优传输的匹配方法来估计具有独立潜变量的线性模型。该方法包括从潜在变量生成伪观测值，从而使模型预测与数据中匹配对应项之间的欧几里得距离最小化。我们证明了我们的非参数估计是一致的，并证明它在模拟数据中表现良好。在收入动态的面板研究中，我们应用这一方法研究了永久性和暂时性收入冲击的周期性。我们发现收入冲击的离散度近似是非周期的，而永久性冲击的偏度是顺周期的。通过比较，我们发现时薪冲击的离散度和偏斜度随经济周期变化不大。
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英文标题：
《Recovering Latent Variables by Matching》
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作者：
Manuel Arellano, Stephane Bonhomme
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
We propose an optimal-transport-based matching method to nonparametrically estimate linear models with independent latent variables. The method consists in generating pseudo-observations from the latent variables, so that the Euclidean distance between the model's predictions and their matched counterparts in the data is minimized. We show that our nonparametric estimator is consistent, and we document that it performs well in simulated data. We apply this method to study the cyclicality of permanent and transitory income shocks in the Panel Study of Income Dynamics. We find that the dispersion of income shocks is approximately acyclical, whereas the skewness of permanent shocks is procyclical. By comparison, we find that the dispersion and skewness of shocks to hourly wages vary little with the business cycle.
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PDF链接：
https://arxiv.org/pdf/1912.13081
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