Welcome
I am a PhD candidate in Economics at Bocconi University, specializing in econometrics.
My research focuses on set identification in micro- and macroeconometric settings. I develop methods for inference under partially credible identifying assumptions and missing data problems.
I am currently on the 2026–27 academic job market.
Job market paper
Sharp Identification for Regression with Interval-Observed and Missing Covariates
Abstract
Researchers frequently estimate regressions when key variables are missing or observed only through intervals. Existing approaches typically resolve this incompleteness through imputation or auxiliary assumptions on the incomplete-data process. We instead ask what the maintained regression model and incomplete data alone imply. For a broad class of conditional moment models, we derive an operational characterization of the sharp identified set, develop a Hausdorff-consistent estimator with uniformly valid confidence regions, and provide tractable computational methods for common regression models. An application to a multiple-price-list design shows that incomplete-data-robust inference can remain informative, while also demonstrating that sharpness matters: non-sharp outer sets can substantially overstate the uncertainty implied by the observed data.