Manuel Arellano

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

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.

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.

We develop methods for testing that an econometric model is underidentified and for estimating the nature of the failed identification.

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.

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

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

DPD98 for Gauss is a programme written in Gauss by Arellano and Bond to estimate dynamic panel data models using GMM.