Doubly-Robust Quantile Treatment Effect Estimation (Job Market Paper)
I develop a doubly-robust estimator of the quantile treatment effect on the treated (QTT). This estimator can obtain consistent estimates of the QTT using either the propensity score or the conditional cdf of the first-differenced untreated outcomes. Aside from the benefits of obtaining consistent estimates of a QTT when a nuisance function is misspecified, there are also efficiency gains. In addition, assumptions on the smoothness of the nuisance parameters can be relaxed when the estimator is doubly-robust. I also show that asymptotically valid confidence intervals can be constructed using the empirical bootstrap. Then, I demonstrate via simulations that my estimator can produce a sharply lower root mean square error compared to other estimators. Finally, I apply my estimator to estimate the effect of increasing the minimum wage on county-level unemployment rates, where I show significant and varied quantile treatment effects.
Econometric Methods for Multiple Fractional Response Variables with a Binary Endogenous Covariate: An Application to Time-Use Data
I develop a model and an estimator for a panel data setting with multiple fractional response variables with a binary endogenous covariate. I develop a two-step technique to obtain consistent estimates of the average partial effects. Then, I provide a variable addition test for endogeneity. I demonstrate using simulations that if the chosen conditional mean function is incorrect, it is still possible to obtain estimates of the average partial effects that are close to the true values. Data from the NLSY97 survey is used to estimate the average partial effect of marriage on how individuals allocate their time within a year.
Works in Progress
Doubly Robust Quantile Treatment Effect Estimation with Staggered Treatment
Quantile Regression for Panel Data with Stable Copulas (with Antonio Galvao)