Research
Working Papers
Multi-Margin Selection in Leniency Designs
Status: Job Market Paper (Draft Coming)
A Locally Robust Semiparametric Approach to Examiner IV Designs
Status: Reject and Resubmit, Journal of Econometrics
Links: arXiv version
Award: 2024 Outstanding Third Year Paper Prize
Abstract
I propose a locally robust semiparametric framework for estimating causal effects using examiner IV designs when adjustment for many or continuous covariates makes saturation infeasible or produces sparse cells. The key ingredient of this approach is an orthogonal moment function that removes the first-order effect of estimation errors in the two treatment regressions defining the examiner IV. I derive the orthogonal moment function and show that it remains valid under misspecification when, for each treatment regression, either that regression or the corresponding Riesz representer in the influence function adjustment is correctly specified. The proposed framework not only allows estimation of the examiner IV using a wide range of nonparametric and machine learning techniques, including LASSO, neural networks and random forests, but also delivers root-n consistent estimation and valid inference under suitable regularity conditions. I examine the finite-sample performance of the estimator through Monte Carlo simulations. I also apply the method to US patent examiners, using regularized regressions to account for differences in technology and other application characteristics.
Nonparametric Testability of Slutsky Symmetry
Status: Submitted
Coauthor: Florian Gunsilius
Links: PDF
Abstract
Economic theory implies strong limitations on what types of consumption behavior are considered rational. Rationality implies that the Slutsky matrix, which captures the substitution effects of compensated price changes on demand for different goods, is symmetric and negative semi-definite. While empirically informed versions of negative semi-definiteness have been shown to be nonparametrically testable, the analogous question for Slutsky symmetry has remained open. Recently, it has even been shown that the symmetry condition is not testable via the average Slutsky matrix, prompting conjectures about its non-testability. We settle this question by deriving nonparametric conditional quantile restrictions on observable data that permit construction of a fully nonparametric test for Slutsky symmetry in an empirical setting with individual heterogeneity and endogeneity. The theoretical contribution is a multivariate generalization of identification results for partial effects in nonseparable models without monotonicity, which is of independent interest. This result has implications for different areas in econometric theory, including nonparametric welfare analysis with individual heterogeneity for which, in the case of more than two goods, the symmetry condition introduces a nonlinear correction factor.
Anatomy of the First Stage with Ordered Treatments
Status: Draft Coming
Work in Progress
Semisupervised Causal Inference with Unstructured Data
Status: Work in Progress
Maximum Overlap Component Analysis
Status: Work in Progress
Coauthor: Florian Gunsilius