arXiv subjects

Kyunghoon Ban

Publications and source records attributed to Kyunghoon Ban.

5 recordsLinked to original sources

arXiv2026

Difference-in-Differences Models in the Presence of Time-Varying Mediators

We study difference-in-differences (DiD) designs in which a binary treatment changes an endogenous time-varying (continuous, discrete, or mixed) mediator that in turn affects an outcome. Under our model assumptions, we show that the usual DiD estimand mixes the average direct effect on the treated, the average indirect effect, and a trend bias term. A two-way fixed effects (TWFE) regression that controls for the mediator does not recover the average direct treatment effect on the treated. We show that a DiD estimand conditional on the observed mediator path identifies the conditional average direct effect for treated units at that path, and that averaging over the treated path distribution identifies the average direct effect even when unconditional parallel trends fails. A stable average mediator effect assumption helps recover the average mediator and indirect effects. The framework extends to multivariate mediators, nonlinear DiD, and multiple treatment periods settings. Existing doubly robust estimators can be used to conduct inference. Revisiting the effects of railroad access on agricultural land values, the specification yields a positive direct component not mediated by measured market access, while the corresponding indirect component is small and imprecise. A TWFE benchmark with the same sample and baseline geographic covariates gives a small, imprecise direct coefficient, whereas the original-control TWFE coefficient reverses sign.

econ.EM↗

arXiv2024

$\texttt{rdid}$ and $\texttt{rdidstag}$: Stata commands for robust difference-in-differences

This article provides a Stata package for the implementation of the robust difference-in-differences (RDID) method developed in Ban and K\'edagni (2023). It contains three main commands: $\texttt{rdid}$, $\texttt{rdid_dy}$, $\texttt{rdidstag}$, which we describe in the introduction and the main text. We illustrate these commands through simulations and empirical examples.

econ.EM↗

arXiv2023

Robust Difference-in-differences Models

The difference-in-differences (DID) method identifies the average treatment effects on the treated (ATT) under mainly the so-called parallel trends (PT) assumption. The most common and widely used approach to justify the PT assumption is the pre-treatment period examination. If a null hypothesis of the same trend in the outcome means for both treatment and control groups in the pre-treatment periods is rejected, researchers believe less in PT and the DID results. This paper develops a robust generalized DID method that utilizes all the information available not only from the pre-treatment periods but also from multiple data sources. Our approach interprets PT in a different way using a notion of selection bias, which enables us to generalize the standard DID estimand by defining an information set that may contain multiple pre-treatment periods or other baseline covariates. Our main assumption states that the selection bias in the post-treatment period lies within the convex hull of all selection biases in the pre-treatment periods. We provide a sufficient condition for this assumption to hold. Based on the baseline information set we construct, we provide an identified set for the ATT that always contains the true ATT under our identifying assumption, and also the standard DID estimand. We extend our proposed approach to multiple treatment periods DID settings. We propose a flexible and easy way to implement the method. Finally, we illustrate our methodology through some numerical and empirical examples.

econ.EM↗

arXiv2021

Nonparametric Bounds on Treatment Effects with Imperfect Instruments

This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection and monotone instrumental variable restrictions, introduced by Manski and Pepper (2000, 2009), jointly imply this assumption. Moreover, we show how the monotone treatment response assumption can help tighten the bounds. The identified set can be written in the form of intersection bounds, which is more conducive to inference. We illustrate our methodology using the National Longitudinal Survey of Young Men data to estimate returns to schooling.

econ.EM↗

arXiv2021

Local Average and Marginal Treatment Effects with a Misclassified Treatment

This paper studies identification of the local average and marginal treatment effects (LATE and MTE) with a misclassified binary treatment variable. We derive bounds on the (generalized) LATE and exploit its relationship with the MTE to further bound the MTE. Indeed, under some standard assumptions, the MTE is a limit of the ratio of the variation in the conditional expectation of the observed outcome given the instrument to the variation in the true propensity score, which is partially identified. We characterize the identified set for the propensity score, and then for the MTE. We show that our LATE bounds are tighter than the existing bounds and that the sign of the MTE is locally identified under some mild regularity conditions. We use our MTE bounds to derive bounds on other commonly used parameters in the literature such as the policy relevant treatment parameter (PRTE) and illustrate the practical relevance of our derived bounds through numerical and empirical results.

econ.EM↗