Professor Stéphane Bonhomme

The aim of this paper is to provide simple nonparametric methods to estimate finite mixture models from data with repeated measurements.

We study the identification of panel models with linear individual-specific coefficients when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness.

In this paper,we construct a nonparametric estimator of the distributions of latent factors in linear independent multi-factor models under the assumption that factor loadings are known.

The authors study linear factor models under the assumptions that factors are mutually independent and independent of errors, and errors can be correlated to some extent.

The authors provide a characterization of the class of weights (or priors) that produce estimators that are first-order unbiased.

In this paper, the authors document whether and how much the equalizing force of earnings mobility has changed in France in the 1990s.

This paper provides a charaterization of the class of weights (or priors) that produce estimators that are first-order unbiased.