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Speaker information

Stephane Bonhomme, Chicago

We propose a 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 the empirical distribution of those latent values is consistent for the population distribution. We illustrate the method on simulated data, and in two applications: nonparametric estimation of the densities of permanent and transitory earnings shocks on panel data from the PSID, and nonparametric estimation of school quality in the Spanish region of Madrid.

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