João Santos Silva graduated in Economics from the Technical University of Lisbon in 1985, and he received a masters degree in Applied Mathematics in 1988 from the same institution. In 1992, he completed his PhD in Economics at the University of Bristol. He taught at the Technical University of Lisbon and at the University of Essex, before joining the School of Economics in 2015. João’s research focuses on theoretical and applied microeconometrics, and he has published in a variety of academic journals, including the Review of Economic Studies, Journal of the American Statistical Association, Review of Economics and Statistics, Journal of Econometrics, and the Journal of Business and Economics Statistics.

Abstract

We consider two nonparametric approaches to ensure that instrumental variables estimators of a linear equation satisfy the rich-covariates condition emphasized by Blandhol et al. (2025), even when the instrument is not unconditionally randomly assigned and the model is not saturated. Both approaches start with a nonparametric estimate of the expectation of the instrument conditional on the covariates, and ensure that the rich-covariates condition is satisfied either by using as the instrument the difference between the original instrument and its estimated conditional expectation, or by adding the estimated conditional expectation to the set of regressors. We derive asymptotic properties of our instrumental variables estimators, and assess their finite sample performance relative to existing approaches using Monte Carlo simulations.

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