Professor Sokbae (Simon) Lee

We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption.

This paper describes three methods for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems.

We investigate state‐dependent effects of fiscal multipliers and allow for endogenous sample splitting to determine whether the U.S. economy is in a slack state.

In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model.

We investigate state-dependent effects of fiscal multipliers and allow for endogenous sample splitting to determine whether the US economy is in a slack state.

We investigate whether growing up in a socialist country affects the development of competitiveness by comparing three Korean groups in South Korea, born and raised in three countries with distinct institutional environments: South Korea, North Korea, and China.

We consider both l0-penalized and l0-constrained quantile regression estimators.

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities.

Datasets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data.

We investigate identification of causal parameters in case-control and related studies.

We compare two groups of the non-student Korean population—native-born South Koreans (SK) and North Korean refugees (NK)—with contrasting institutional and cultural backgrounds. In our experiment, the subjects play dictator games under three different treatments in which the income source varies: first, the income is randomly given to the subject; second, it is earned by the subject; third, it is individually earned by the subject and an anonymous partner and then pooled together.

We compare two groups of the non-student Korean population—native-born South Koreans (SK) and North Korean refugees (NK)—with contrasting institutional and cultural backgrounds.

We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability.

This paper examines the trends in geographic localization of knowledge spillovers via patent citations, extracting multiple cohorts of new sample US patents from the period of 1976-2015.

This paper describes a method for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems. The optimization problems arise in applications in which grouped data are used for estimation of a model's structural parameters. The parameters are characterized by restrictions that involve the population means of observed random variables in addition to the structural parameters of interest. Inference consists of finding confidence intervals for the structural parameters. Our method is non-asymptotic in the sense that it provides a finite-sample bound on the difference between the true and nominal probabilities with which a confidence interval contains the true but unknown value of a parameter. We contrast our method with an alternative non-asymptotic method based on the median-of-means estimator of Minsker (2015). The results of Monte Carlo experiments and an empirical example illustrate the usefulness of our method.

In a randomized control trial, the precision of an average treatment effect estimator and the power of the corresponding t-test can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. To design the experiment, a researcher needs to solve this tradeoff subject to her budget constraint. We show that this optimization problem is equivalent to optimally predicting outcomes by the covariates, which in turn can be solved using existing machine learning techniques using pre-experimental data such as other similar studies, a census, or a household survey. In two empirical applications, we show that our procedure can lead to reductions of up to 58% in the costs of data collection, or improvements of the same magnitude in the precision of the treatment effect estimator.

Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently unordered monotonicity. We show how treatment eects can be identied in a more general class of models that allows for multidimensional unobserved heterogeneity.

We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability.

Using the Reinhart–Rogoff dataset, we find a debt threshold not around 90 per cent but around 30 per cent, above which the median real gross domestic product (GDP) growth falls abruptly.