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