Welcome! I am a PhD candidate at the University of Amsterdam and the Tinbergen Institute. I will be available on the 2023/24 academic job market.
My research advances the econometric methods in the field of causal inference with panel data. In my job market paper, I develop strategies to identify the mechanisms behind treatment effects in difference-in-differences designs. My current papers have applications in environmental economics, economic history and public health.
My advisors are Frank Kleibergen and Andreas Pick. Last spring I was a Visiting Research Fellow at Brown University, invited by Toru Kitagawa. Before my graduate studies I was a Junior Research Manager at the Center for Evaluation and Development and I obtained my bachelor’s degree at the University of Mannheim.
Placement director: Prof. Eric Bartelsman (e.j.bartelsman[at]vu.nl)
Placement assistant: Christina Månsson (c.mansson[at]tinbergen.nl)
Field (primary): Econometrics
Field (secondary): Causal inference, Applied microeconometrics
Mediation Analysis in Difference-in-Differences Designs  (honorable mention at the IAAE 2023)
Abstract: This paper develops strategies to understand the mechanisms behind treatment effects in difference-in-differences (DiD) designs. Building on concepts from mediation analysis, I present identification strategies for the part of the average treatment effect that is caused by the treatment affecting a mediating variable. The sequential DiD approach requires additional parallel trend assumptions, a restriction on the mediator effect heterogeneity, and monotonicity of the treatment effect on the mediator. To avoid some of these restrictions, I present a two-sample approach, which includes results from other studies. I propose robust inference procedures on the proportion of the total effect a particular channel can explain. I revisit two empirical studies to show how researchers can use these approaches in practice.
Time-Weighted Difference-in-Differences: Accounting for Common Factors in Short T Panels [WP2] (R&R Journal of Business & Economic Statistics)
Abstract: I propose a time-weighted difference-in-differences (TWDID) estimation approach that is robust against time-varying common factors in short T panels. Time weighting substantially reduces both bias and variance compared to an unweighted DID estimator through balancing the pre-treatment and post-treatment factors. To conduct valid inference on the average treatment effect, I develop a correction term that adjusts conventional standard errors for the presence of weight estimation uncertainty. Revisiting a study on the effect of a cap-and-trade program on NOx emissions, TWDID estimation reduces the standard errors of the estimated treatment effect by 10% compared to a conventional DID approach.
|Undergraduate||U Amsterdam: Econometricst,c, Mathematical and Empirical Financet, Empirical Projectt,c, Thesis supervision
U Mannheim: Microeconomics At,r, Statistics 1t,c, Mathematics for Economistst,r
|Graduate||U Amsterdam: Advanced Econometrics 1c, Financial Econometricsc
Tinbergen Institute: Measure Theory and Asymptotic Statisticst, Microeconomics 3: Information and Contract Theoryt
|Other||Causal inference (TA for an online class by Scott Cunningham); Introduction to STATA (Crash course for C4ED employees)|
t theory tutorial (around 30 students), c computer lab (30), r revision lecture (80)
Paper presented: JMP
Paper presented: WP2