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Recovering Latent Variables by Matching

Manuel Arellano, Stephane Bonhomme

arXiv 30 Dec 2019 · Econometrics · publishedJournal of the American Statistical Association (2021) · 5 citations (OpenAlex)

arXiv:1912.13081 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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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Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Quasi-Bayes in Latent Variable Models0.92843
2An econometrician's guide to optimal transport0.51121
3Identification and estimation of dynamic random coefficient models0.00051