Heshani Madigasekara, D. S. Poskitt, Lina Zhang, Xueyan Zhao
arXiv 7 Nov 2024 · Econometrics
arXiv:2411.04450 · PDF · DOI · OpenAlex · Extracted main text
This paper aims to partially identify the distributional treatment effects (DTEs) that depend on the unknown joint distribution of treated and untreated potential outcomes. We construct the DTE bounds using panel data and allow individuals to switch between the treated and untreated states more than once over time. Individuals are grouped based on their past treatment history, and DTEs are allowed to be heterogeneous across different groups. We provide two alternative group-wise copula equality assumptions to bound the unknown joint and the DTEs, both of which leverage information from the past observations. Testability of these two assumptions are also discussed, and test results are presented. We apply this method to study the treatment effect heterogeneity of exercising on the adults' body weight. These results demonstrate that our method improves the identification power of the DTE bounds compared to the existing methods.
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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 | Callaway, B (2021) Bounds on distributional treatment effect parameters using panel data with an application on job displacement | 1.000 | 33 | 4 | 100% |
| 2 | Williamson, R. C. and T. Downs (1990) Probabilistic arithmetic | 1.000 | 7 | 3 | 100% |
| 3 | Fan, Y. and S. S. Park (2010) Sharp bounds on the distribution of treatment effects and their statistical inference | 0.843 | 3 | 3 | 100% |
| 4 | Frandsen, B. R. and L. J. Lefgren (2021) Partial identification of the distribution of treatment effects with an application to the Knowledge is Power Program (KIPP) | 0.811 | 4 | 2 | 100% |
| 5 | Hong, H. and J. Li (2018) The numerical delta method | 0.811 | 4 | 2 | 100% |
| 6 | HILDA Survey, Wave 15 (2016) The Household, Income and Labour Dynamics in Australia (HILDA) Survey, GENERAL RELEASE 15 (Waves 1-15), Department of Social Ser… | 0.737 | 3 | 2 | 100% |
| 7 | Flores, C. A., X. Chen, et al (2018) Average treatment effect bounds with an instrumental variable: theory and practice | 0.737 | 3 | 2 | 100% |
| 8 | Rémillard, B. and O. Scaillet (2009) Testing for equality between two copulas | 0.737 | 3 | 2 | 100% |
| 9 | Sklar, M (1959) Fonctions de repartition an dimensions et leurs marges | 0.644 | 4 | 1 | 100% |
| 10 | Callaway, B. and T. Li (2019) Quantile treatment effects in difference in differences models with panel data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 30 scored citations.