Year: 2025
Type: article
Abstract: This paper introduces parallel trends forest, a novel approach to selecting optimal control samples when using difference-indifferences (DiD) in a relatively long panel data with little randomization in treatment assignment. Our method uses machine learning techniques to find control units that best meet the parallel trends assumption. We demonstrate that our approach outperforms existing methods, particularly with noisy, granular data. Applying the parallel trends forest to analyze the impact of post-trade transparency in corporate bond markets, we find that it produces more robust estimates compared to traditional two-way fixed effects models. Our results suggest that the effect of transparency on bond turnover is small and not statistically significant when allowing for constrained deviations from parallel trends. This method offers researchers a powerful tool for conducting more reliable DiD analyses in complex, real-world settings.
Authors: Yesol Huh, Matthew Vanderpool Kling
Institutions: Federal Reserve, Federal Reserve Board of Governors
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415008080
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