Recovering latent variables by matching

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP2/20

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
• Subject: Latent variables ; nonparametric estimation ; matching ; factor models ; optimaltransport ; income dynamics

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Arellano, Manuel ; Bonhomme, Stéphane
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2020

Further information

• DOI: doi:10.1920/wp.cem.2020.220
• Handle: http://hdl.handle.net/10419/241877
• Last update: 10.03.2025, 11:44 AM CET