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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.
Authors
Research Fellow Centre for Monetary and Financial Studies (CEMFI)
Manuel is a Research Fellow of the IFS and a Professor of Econometrics at CEMFI, Madrid.
Professor of Economics University of Chicago
Working Paper details
- DOI
- 10.1920/wp.cem.2020.220
- Publisher
- The IFS
Suggested citation
Arellano, M and Bonhomme, S. (2020). Recovering Latent Variables by Matching. London: The IFS. Available at: https://ifs.org.uk/publications/recovering-latent-variables-matching (accessed: 4 May 2024).
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