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Heinrich Heine University

Academic institutioneurope · de
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Selected work

Representative Papers

Inference in Unbalanced Panel Data Models with Interactive Fixed Effects

Apr 07, 2020

This paper addresses asymptotic inference for interactive fixed-effects estimators in unbalanced panel data under random missingness. Recognizing that existing literature lacks a systematic characterization of how missingness proportions and patterns affect estimation, we derive the asymptotic normality of the estimator under general missing-data mechanisms—establishing the first rigorous theoretical foundation for this setting. We propose a robust inference procedure based on principal component analysis (PCA) that remains valid under high missingness rates. Monte Carlo simulations confirm the method’s reliability even with substantial missingness and demonstrate the robustness of Bai (2009) and Moon–Weidner (2017) frameworks under conditionally random missingness. Applying our approach to reassess the causal effect of democratization on economic growth, we robustly identify a statistically significant positive impact. Our results enhance both the statistical credibility and empirical applicability of interactive fixed-effects models in realistic settings with missing data.

3 citationsRead paper

Learning control variables and instruments for causal analysis in observational data

Jul 05, 2024

Estimating causal effects from observational data requires selecting appropriate control and instrumental variables that satisfy causal identification conditions—a challenging task often reliant on strong domain knowledge or ad hoc assumptions. Method: This paper proposes the first end-to-end joint learning framework that automatically identifies valid combinations of control and instrumental variables. Grounded in conditional independence testing, the method integrates nonparametric dependence measures with structural search optimization, ensuring statistical consistency in variable selection under mild regularity conditions. Contribution/Results: Unlike conventional approaches requiring prespecified variable sets or strong prior assumptions, our framework is fully data-driven. In simulations, it achieves significantly higher variable identification accuracy. Empirically, applied to the Job Corps study, its estimated treatment effect closely aligns with results from the randomized controlled trial—demonstrating both validity and robustness in real-world causal inference.

1 citationsRead paper
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