Recovering Latent Variables by Matching

• 成果类型: Article
• 署名作者: Arellano, Manuel; Bonhomme, Stephane
• 署名单位: University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1952877
• 发表日期: 2023
• 页码: 693-706
• 关键词: DENSITY-ESTIMATION; panel-data; nonparametric deconvolution; semiparametric estimation; measurement error; Optimal Transport; nonlinear models; Optimal Rates; earnings; distributions

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. for this article are available online.