arXiv 18 Dec 2019 · Econometrics
arXiv:1912.08772 · PDF · DOI · OpenAlex · Extracted main text
We analyze different types of simulations that applied researchers can use to assess whether their inference methods reliably control false-positive rates. We show that different assessments involve trade-offs, varying in the types of problems they may detect, finite-sample performance, susceptibility to sequential-testing distortions, susceptibility to cherry-picking, and implementation complexity. We also show that a commonly used simulation to assess inference methods in shift-share designs can lead to misleading conclusions and propose alternatives. Overall, we provide novel insights and recommendations for applied researchers on how to choose, implement, and interpret inference assessments in their empirical applications.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Adão, R., Kolesar, M., and Morales, E (2019) Shift-Share Designs: Theory and Inference* | 0.916 | 26 | 5 | 77% |
| 2 | Advani, A., Kitagawa, T., and Stczynski, T (2019) Mostly harmless simulations? using monte carlo studies for estimator selection | 0.874 | 5 | 2 | 100% |
| 3 | Autor, D. H., Dorn, D., and Hanson, G. H (2013) The china syndrome: Local labor market effects of import competition in the united states | 0.822 | 9 | 3 | 56% |
| 4 | Bertrand, M., Duflo, E., and Mullainathan, S (2004) How much should we trust differences-in-differences estimates? | 0.811 | 4 | 2 | 100% |
| 5 | Dix-Carneiro, R., Soares, R. R., and Ulyssea, G (2018) Economic shocks and crime: Evidence from the brazilian trade liberalization | 0.794 | 10 | 4 | 50% |
| 6 | Acemoglu, D. and Restrepo, P (2020) Robots and jobs: Evidence from us labor markets | 0.794 | 10 | 3 | 50% |
| 7 | Abadie, A., Athey, S., Imbens, G. W., and Wooldridge, J. M (2020) Sampling-based versus design-based uncertainty in regression analysis | 0.737 | 3 | 2 | 100% |
| 8 | Alvarez, L., Ferman, B., and Wüthrich, K (2025) Inference with few treated units self | 0.644 | 4 | 1 | 100% |
| 9 | Conley, T. G. and Taber, C. R (2011) Inference with Difference-in-Differences with a Small Number of Policy Changes | 0.644 | 2 | 2 | 100% |
| 10 | Athey, S., Imbens, G., Metzger, J., and Munro, E (2020) Using wasserstein generative adversarial networks for the design of monte carlo simulations | 0.644 | 2 | 2 | 100% |
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