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Quantile and Distribution Treatment Effects on the Treated with Possibly Non-Continuous Outcomes

Nelly K. Djuazon, Emmanuel Selorm Tsyawo

arXiv 14 Aug 2024 · Econometrics

arXiv:2408.07842 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Quantile and Distribution Treatment effects on the Treated (QTT/DTT) for non-continuous outcomes are either not identified or inference thereon is infeasible using existing methods. By introducing functional index parallel trends and no anticipation assumptions, this paper identifies and provides uniform inference procedures for QTT/DTT. The inference procedure applies under both the canonical two-group and staggered treatment designs with balanced panels, unbalanced panels, or repeated cross-sections. Monte Carlo experiments demonstrate the proposed method's robust and competitive performance, while an empirical application illustrates its practical utility.

Citation extraction

29
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101
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distinct cited
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Chernozhukov, Victor, Fernandez-Val, Ivan, Melly, Blaise, Wüthrich,… (2019) Generic inference on quantile and quantile effect functions for discrete outcomes0.9568588%
2Di Tella, Rafael, Schargrodsky, Ernesto (2004) Do police reduce crime? Estimates using the allocation of police forces after a terrorist attack0.9507486%
3Wooldridge, Jeffrey M (2023) Simple approaches to nonlinear difference-in-differences with panel data0.94613585%
4Ghanem, Dalia, Kédagni, Désiré, Mourifié, Ismael (2023) Evaluating the impact of regulatory policies on social welfare in difference-in-difference settings0.87482100%
5Gutknecht, Daniel, Liu, Cenchen (2024) Generalized difference-in-differences for ordered choice models: Too many "False Zeros"?0.87472100%
6Roth, Jonathan, Sant'Anna, Pedro HC (2023) When is parallel trends sensitive to functional form?0.87472100%
7Callaway, Brantly, Li, Tong (2019) Quantile treatment effects in difference in differences models with panel data0.87462100%
8Athey, Susan, Imbens, Guido (2006) Identification and inference in nonlinear difference-in-differences models0.87452100%
9Chernozhukov, Victor, Fernandez-Val, Ivan, Melly, Blaise (2013) Inference on counterfactual distributions0.87452100%
10Yamauchi, Soichiro (2020) Difference-in-differences for ordinal outcomes: Application to the effect of mass shootings on attitudes toward gun control0.81142100%

Showing the top 10 of 29 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1A Grid-Rate Condition for Valid Uniform Inference0.00011