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Parallel Trends Forest: Data-Driven Control Sample Selection in Difference-in-Differences

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Abstract

This paper introduces parallel trends forest, a novel approach to selecting optimal control samples when using difference-in-differences (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.

Suggested Citation

  • Yesol Huh & Matthew Kling, 2025. "Parallel Trends Forest: Data-Driven Control Sample Selection in Difference-in-Differences," Finance and Economics Discussion Series 2025-091, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2025-91
    DOI: 10.17016/FEDS.2025.091
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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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