arXiv 21 Jan 2024 · Econometrics
arXiv:2401.11422 · PDF · DOI · OpenAlex · Extracted main text
In the instrumental variable quantile regression (IVQR) model of Chernozhukov and Hansen (2005), a one-dimensional unobserved rank variable monotonically determines a single potential outcome. Even when multiple outcomes are simultaneously of interest, it is common to apply the IVQR model to each of them separately. This practice implicitly assumes that the rank variable of each regression model affects only the corresponding outcome and does not affect the other outcomes. In reality, however, it is often the case that all rank variables together determine the outcomes, which leads to a systematic correlation between the outcomes. To deal with this, we propose a nonlinear IV model that allows for multivariate unobserved heterogeneity, each of which is considered as a rank variable for an observed outcome. We show that the structural function of our model is locally identified under the assumption that the IV and the treatment variable are sufficiently positively correlated.
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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 | Chernozhukov, Victor, Hansen, Christian (2005) An IV model of quantile treatment effects | 1.000 | 22 | 5 | 100% |
| 2 | Chernozhukov, Victor, Hansen, Christian (2004) The effects of 401 (k) participation on the wealth distribution: an instrumental quantile regression analysis | 0.874 | 5 | 2 | 100% |
| 3 | Ghosal, Promit, Sen, Bodhisattva (2022) Multivariate ranks and quantiles using optimal transport: Consistency, rates and nonparametric testing | 0.843 | 4 | 3 | 75% |
| 4 | Chernozhukov, Victor, Galichon, Alfred, Hallin, Marc, Henry, Marc (2017) Monge-Kantorovich Depth, Quantiles, Ranks, and Signs | 0.811 | 4 | 2 | 100% |
| 5 | Hallin, Marc, Barrio, Eustasio, Cuesta-Albertos, Juan, Matrán, Carlos (2021) Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach | 0.811 | 4 | 2 | 100% |
| 6 | Chen, Xiaohong, Chernozhukov, Victor, Lee, Sokbae, Newey, Whitney K (2014) Local identification of nonparametric and semiparametric models | 0.644 | 3 | 2 | 67% |
| 7 | Chernozhukov, Victor, Hansen, Christian (2013) Quantile models with endogeneity | 0.644 | 2 | 2 | 100% |
| 8 | Villani, Cédric (2003) Topics in Optimal Transportation | 0.644 | 2 | 2 | 100% |
| 9 | Carlier, Guillaume, Chernozhukov, Victor, Galichon, Alfred (2016) Vector Quantile Regression: An Optimal Transport Approach | 0.511 | 2 | 1 | 100% |
| 10 | Ekeland, Ivar, Galichon, Alfred, Henry, Marc (2012) Comonotonic measures of multivariate risks | 0.511 | 2 | 1 | 100% |
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