arXiv subjects

Diego Ciccia

Publications and source records attributed to Diego Ciccia.

3 recordsLinked to original sources

arXiv2026

Difference-in-Differences Estimators When No Unit Remains Untreated

We study treatment-effect estimation in two-period panels where all units are untreated initially and receive strictly positive treatment doses in the second period. With quasi-untreated units receiving doses local to zero, we show that, under parallel trends, a weighted average of potential-outcome slopes is identified by a difference-in-differences estimand using quasi-untreated units as controls, and we propose a nonparametric estimator based on regression-discontinuity methods. We then develop estimators for settings without quasi-untreated units and propose a test of the homogeneous-effect assumption underlying two-way fixed-effects regressions.

econ.EM↗

arXiv2025

Using did_multiplegt_dyn to Estimate Event-Study Effects in Complex Designs: Overview, and Four Examples Based on Real Datasets

The command did_multiplegt_dyn can be used to estimate event-study effects in complex designs with a potentially non-binary and/or non-absorbing treatment. This paper starts by providing an overview of the estimators computed by the command. Then, simulations based on three real datasets are used to demonstrate the estimators' properties. Finally, the command is used on four real datasets to estimate event-study effects in complex designs. The first example has a binary treatment that can turn on an off. The second example has a continuous absorbing treatment. The third example has a discrete multivalued treatment that can increase or decrease multiple times over time. The fourth example has two, binary and absorbing treatments, where the second treatment always happens after the first.

econ.EM↗

arXiv2024

A Short Note on Event-Study Synthetic Difference-in-Differences Estimators

I propose an event study extension of Synthetic Difference-in-Differences (SDID) estimators. I show that, in simple and staggered adoption designs, estimators from Arkhangelsky et al. (2021) can be disaggregated into dynamic treatment effect estimators, comparing the lagged outcome differentials of treated and synthetic controls to their pre-treatment average. Estimators presented in this note can be computed using the sdid_event Stata package.

econ.EM↗