Professor Stéphane Bonhomme

Economists are often interested in estimating averages with respect to distributions of unobservables, such as moments of individual fixed-effects, or average partial effects in discrete choice models.

Economists are often interested in estimating averages with respect to distributions of unobservables. Examples are moments of individual fixed-effects, average partial effects in discrete choice models, and counterfactual simulations in structural models.

We propose a framework for estimation and inference when the model may be misspecified.

We propose an optimal-transport-based matching method to nonparametrically estimate linear models with independent latent variables.

Economists are often interested in estimating averages with respect to distributions of unobservables. Examples are moments of individual fixed-effects, average effects in discrete choice models, or counterfactual simulations in structural models.

We propose a framework for estimation and inference about the parameters of an economic model and predictions based on it, when the model may be misspecified.

Journal in American Economic Association

We develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption.

Discretizing unobserved heterogeneity

Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identification and flexible estimation in the presence of time-invariant and time-varying latent variables.

We introduce a class of quantile regression estimators for short panels.

We propose a method to correct for sample selection in quantile regression models. Selection is modelled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method-of-moments criterion. Given these parameter estimates, the percentile levels of the outcome are re-adjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated “check” function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978-2000. We also extend the method to account for the presence of equilibrium effects when performing counterfactual exercises.

The authors develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption.

The authors develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption.

In this paper, the authors introduce a class of quantile regression estimators for short panels.

The authors present a constructive identification proof of p-linear decompositions of q-way arrays. The analysis is based on the joint spectral decomposition of a set of matrices.