Research

Publications

1. "Recent Applications of Generalized Instrumental Variable Models" (2025)

Seoul Journal of Economics, 2025, Volume 38, Number 1, Special Issue (invited contribution) 

2. "Nonparametric Estimation of Sponsored Search Auctions and Impacts of Ad Quality on Search Revenue" (2025, with Pallavi Pal)

Management Science (2025) [Cemmap working paper CWP16/24] [CESifo Working Paper No. 10312]

3. "Powerful t-tests in the presence of nonclassical measurement error" (2024, with Daniel Wilhelm)

Econometric Reviews, Vol. 43, NO. 6, 345–378 [Cemmap working paper CWP22/23]

4. "IV methods for Tobit models" (2023, with Andrew Chesher and Adam Rosen) 

Journal of Econometrics, Volume 235, Issue 2, pages 1700-1724 [Cemmap working paper CWP16/22]

5. "Partially Identifying Competing Risks Models: an Application to the War on Cancer" (2023)

Journal of Econometrics, Volume 234, Issue 2, pages 536-564 [Working paper version]

6. "COVID-19 Vaccination Mandates and Vaccine Uptake" (2022, with Alexander Karaivanov, Shih En Lu, and Hitoshi Shigeoka)

Nature Human Behaviour, 6, 1615–1624 (2022 Impact Factor: 29.9) [MedRxiv preprint] [NBER WP] [IZA WP] 

7. "Vaccination strategies and transmission of COVID-19: evidence across advanced countries" (2022, with Young Jun Lee)

Journal of Health Economics, Volume 82, 102589 [arXiv:2109.06453] [Cemmap working paper CWP38/21]

8. "An Adaptive Test of Stochastic Monotonicity" (2021, with Denis Chetverikov and Daniel Wilhelm)

Econometric Theory, Volume 37, Issue 3, pp. 495 - 536 [Cemmap working paper CWP17/20]

9. "Partial identification in nonseparable count data IV models" (2020)

Econometrics Journal, Volume 23, Issue 2, Pages 232–250 [Working paper version]

10. "Nonparametric instrumental variable estimation" (2018, with Denis Chetverikov and Daniel Wilhelm)

Stata Journal, Volume 18 Number 4: pp. 937-950 [Cemmap working paper CWP47/17] 

[Stata package: type "ssc install npiv" in your Stata console]

Working Papers

1. "Semi-nonparametric Models of Multidimensional Matching: an Optimal Transport Approach" (2024, with Young Jun Lee), revision requested at Journal of Econometrics [Cemmap working paper CWP12/24]

Abstract : This paper proposes empirically tractable multidimensional matching models, focusing on worker-job matching. We generalize the parametric model proposed by Lindenlaub (2017, Review of Economic Studies), which relies on the assumption of joint normality of observed characteristics of workers and jobs. In our paper, we allow unrestricted distributions of characteristics and show identification of the production technology, and equilibrium wage and matching functions using tools from optimal transport theory. Given identification, we propose efficient, consistent, asymptotically normal sieve estimators. We revisit Lindenlaub's empirical application and show that, between 1990 and 2010, the U.S. economy experienced much larger technological progress favoring cognitive abilities than the original findings suggest. Furthermore, our flexible model specifications provide a significantly better fit for patterns in the evolution of wage inequality.

2. "Point-Identifying Semiparametric Sample Selection Models with No Excluded Variable" (2025, with Young Jun Lee), revision requested at Journal of Business and Economic Statistics [Cemmap working paper CWP07/25]

Abstract: Sample selection is pervasive in applied economic studies. This paper develops semiparametric selection models that achieve point identification without relying on exclusion restrictions, an assumption long considered necessary for identification in semiparametric selection models. Our identification conditions require at least one continuously distributed covariate and certain nonlinearity in the selection process. We propose a two-step plug-in estimator that is root-n-consistent, asymptotically normal, and computationally straightforward (readily available in statistical softwares), allowing for heteroskedasticity. Our approach provides a middle ground between Lee (2009, Restud)'s nonparametric bounds and Honoré and Hu (2020, Econometrica)'s linear selection bounds, while ensuring point identification. Simulation evidence confirms its excellent finite-sample performance. We apply our method to estimate the racial and gender wage disparity using data from the US Current Population Survey. Our estimates tend to lie outside the Honoré and Hu bounds.