How Much Should We Trust Staggered Difference-In-Differences Estimates?

ArXiv ID: ssrn-3794018 “View on arXiv”

Authors: Unknown

Abstract

We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These bi

Keywords: Difference-in-Differences (DiD), Policy Evaluation, Econometric Bias, Causal Inference, Staggered Adoption, Multi-Asset (Quantitative Research)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper involves advanced econometric theory on staggered difference-in-differences and discusses complex estimator derivations, but it is primarily a theoretical/methodological critique without original backtesting or heavy data implementation.

  flowchart TD
    A["Research Question:<br>How much should we trust staggered<br>DID estimates?"] --> B["Methodology: Simulation & Analytical Framework"]
    B --> C{"Data / Inputs"}
    C --> C1["Multi-Asset Dataset"]
    C --> C2["Policy Adoption<br>Staggered Design"]
    C --> C3["Treatment Effects<br>(Heterogeneity)"]
    C --> C4["Distributional Assumptions"]
    
    C1 & C2 & C3 & C4 --> D["Computational Process:<br>Estimation of Staggered DID"]
    D --> D1["Standard TWFE Estimator"]
    D --> D2["New (Robust) Estimators"]
    
    D1 --> E{"Analysis"}
    D2 --> E
    
    E --> F["Key Findings / Outcomes"]
    F --> F1["Bias Detection:<br>Standard TWFE often biased"]
    F --> F2["Solution:<br>Use robust estimators<br>e.g., Callaway & Sant'Anna"]
    F --> F3["Conclusion:<br>Trust estimates only after<br>robustness checks"]