Hello. My name is Joonho (Phil) Hwang (황준호; 黃俊晧), and I am a fifth-year PhD student in economics at Seoul National University, where I am fortunate to be advised by Professor Seojeong Lee. My research interests are in econometrics, with a particular focus on

• panel data models
• causal inference

Working papers

Online Updating for Linear Panel Regressions
joint with Seojeong Lee
🏆 The Best Third-Year Paper Award in Department of Economics @ SNU

ABS

In this paper, we develop online updating methods for linear panel regression models. Online updating refers to procedures for sequentially updating parameter estimates as new data become available. In practice, the potential size of the dataset or data confidentiality constraints may preclude researchers from storing or accessing the entire dataset. We propose an online updating procedure for widely used linear regression models in panel data, where data expansion can occur through either (1) the arrival of new units or (2) the arrival of additional time periods for existing units. The proposed procedure yields closed-form expressions for updating both the point estimates and associated standard errors in each scenario.

Presented at: SNU Econometrics Workshop, SETA 2025, University of Sydney, KERIC 2025, SNU Workshop on Recent Advances in Econometrics

On the Bracketing Relationship in Staggered Treatment Designs
Submitted

ABS

Researchers often use fixed-effects and lagged-dependent-variable (LDV) estimates as upper and lower bounds on a treatment effect. This paper shows that this bracketing relationship can fail in staggered treatment designs. In staggered two-way fixed effects settings, neither estimator necessarily bounds the true group-time average treatment effect. Monte Carlo simulations show that such failures are common. The results suggest caution in using fixed-effects and LDV estimates as informal bounds in staggered-adoption settings.

PDF SSRN

Work in progress

Deep Panel Quantile Regression (with Chencheng Fang and Gayeon Hong)

Synthetic Difference-in-Differences with Missing Post-Treatment Outcomes (with Chencheng Fang)