2021年11月23日上午9点，我院“暨南论道”公开系列讲座第35期于线上顺利举办，美国耶鲁大学陈晓红教授受邀担任本期演讲嘉宾。陈晓红现任美国耶鲁大学 Malcolm K. Brachman 经济学教授，Journal of Econometrics主编。2007年入选世界计量经济学会院士，2019年入选美国艺术与科学学院院士，是计量经济学领域顶尖的学者之一。讲座由我院院长冯帅章教授与美国约翰·霍普金斯大学胡颖尧教授共同主持。

Abstract:We introduce computationally simple, data-driven procedures for estimation and inference on a structural function h0 and its derivatives in nonparametric models using instrumental variables. Our first procedure is a bootstrap-based, data-driven choice of sieve dimension for sieve nonparametric instrumental variables (NPIV) estimators. When implemented with this data-driven choice, sieve NPIV estimators of h0 and its derivatives are adaptive: they converge at the best possible (i.e., minimax) sup-norm rate, without having to know the smoothness of h0, degree of endogeneity of the regressors, or instrument strength. Our second procedure is a data-driven approach for constructing honest and adaptive uniform confidence bands (UCBs) for h0 and its derivatives. Our data-driven UCBs guarantee coverage for h0 and its derivatives uniformly over a generic class of data-generating processes (honesty) and contract at, or within a logarithmic factor of, the minimax sup-norm rate (adaptivity). As such, our data-driven UCBs deliver asymptotic efficiency gains relative to UCBs constructed via the usual approach of undersmoothing. In addition, both our procedures apply to nonparametric regression as a special case. We use our procedures to estimate and perform inference on a nonparametric gravity equation for the intensive margin of firm exports and find evidence against common parameterizations of the distribution of unobserved firm productivity.