Sokbae Lee is a Professor at Columbia University and a Research Fellow at the IFS. He also works with the the Centre of Microdata Methods and Practice (cemmap) and is a Professor in the department of economics at Columbia University. His research focuses on theoretical and applied econometrics.
We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set.
This paper extends the method of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to the estimation of not only means, but also distributions of potential outcomes.
This paper extends the method of local instrumental variables developed by Heckman and Vytlacil to the estimation of not only means, but also distributions of potential outcomes.
This paper presents a new estimator for the mixed proportional hazard model that allows for a nonparametric baseline hazard and time-varying regressors.
We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero.
This paper is concerned with inference about a function <i>g</i> that is identified by a conditional quantile restriction involving instrumental variables.
This paper develops a concrete formula for the asymptotic distribution of two-step,
possibly non-smooth semiparametric M-estimators under general misspecification.
We consider nonparametric estimation of a regression function that is identified by requiring a specified quantile of the regression "error" conditional on an instrumental variable to be zero.
