Article
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The Differential Error Components Models under Treatment Effect Heterogeneity
Version 1
: Received: 6 July 2024 / Approved: 6 July 2024 / Online: 8 July 2024 (09:04:22 CEST)
How to cite: Jin, Y. The Differential Error Components Models under Treatment Effect Heterogeneity. Preprints 2024, 2024070569. https://doi.org/10.20944/preprints202407.0569.v1 Jin, Y. The Differential Error Components Models under Treatment Effect Heterogeneity. Preprints 2024, 2024070569. https://doi.org/10.20944/preprints202407.0569.v1
Abstract
The fixed and random effects models are popular methods for identifying the causal effects of treatment. First, I introduce the two-way fixed effects model and show a class of the estimators for the random effects models. Misidentification may occur in these models due to the treatment effect heterogeneity. Then, I propose the differential error component models to address such problems while the models exclude the fixed effects of units and time periods and consider the random effects of units and time periods. Meanwhile, I present a simulation of a staggered design and revisit the application in (De Chaisemartin and D'Haultfoeuille, 2020). The results demonstrate that one additional newspaper increases the 0.31% average effect of the presidential turnout and the 1.05% average effect when the newspapers’ changes are non-negative, where the proposed estimators are robust and efficient.
Keywords
Generalized least square; Error component; Fixed effects; Random effects; Treatment effect heterogeneity
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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