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Estimation and Inference of Average Treatment Effect in Percentage Points under Heterogeneity

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  • Ying Zeng

Abstract

In semi-log regression models with heterogeneous treatment effects, the average treatment effect (ATE) in log points and its exponential transformation minus one underestimate the ATE in percentage points. I propose new estimation and inference methods for the ATE in percentage points, with inference utilizing the Fenton-Wilkinson approximation. These methods are particularly relevant for staggered difference-in-differences designs, where treatment effects often vary across groups and periods. I prove the methods' large-sample properties and demonstrate their finite-sample performance through simulations, revealing substantial discrepancies between conventional and proposed measures. Two empirical applications further underscore the practical importance of these methods.

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  • Ying Zeng, 2024. "Estimation and Inference of Average Treatment Effect in Percentage Points under Heterogeneity," Papers 2408.06624, arXiv.org.
    • Handle: RePEc:arx:papers:2408.06624
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    References listed on IDEAS

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