arXiv 13 Jan 2020 · Statistics — Methodology · publishedJournal of Business and Economic Statistics (2022) · 2 citations (OpenAlex)
arXiv:2001.04324 · PDF · DOI · OpenAlex · Extracted main text
In this study, we develop a novel estimation method for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and proposes a parametric estimation based on the minimum distance method. However, when the dimensionality of the covariates is large, the minimum distance estimation using this process is computationally demanding. To overcome this problem, we propose a two-step estimation method based on the quantile regression and minimum distance methods. We then show the uniform asymptotic properties of our estimator and the validity of the nonparametric bootstrap. The Monte Carlo studies indicate that our estimator performs well in finite samples. Finally, we present two empirical illustrations, to estimate the distributional effects of insurance provision on household production and TV watching on child cognitive development.
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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 | Ishihara, T (2020) Identification and estimation of time-varying nonseparable panel data models without stayers self | 1.000 | 18 | 5 | 100% |
| 2 | Athey, S. and G. W. Imbens (2006) Identification and inference in nonlinear difference-in-differences models | 1.000 | 7 | 3 | 100% |
| 3 | Chernozhukov, V. and C. Hansen (2006) Instrumental quantile regression inference for structural and treatment effect models | 0.928 | 5 | 3 | 80% |
| 4 | Chernozhukov, V. and C. Hansen (2005) An IV model of quantile treatment effects | 0.874 | 5 | 2 | 100% |
| 5 | Chernozhukov, V., I. Fernández-Val, J. Hahn, and W. Newey (2013) Average and quantile effects in nonseparable panel models | 0.811 | 4 | 2 | 100% |
| 6 | D'Haultfuille, X., S. Hoderlein, and Y. Sasaki (2013) Nonlinear difference-in-differences in repeated cross sections with continuous treatments, Tech | 0.811 | 4 | 2 | 100% |
| 7 | Chernozhukov, V., I. Fernandez-Val, S. Hoderlein, H. Holzmann, and W… (2015) Nonparametric identification in panels using quantiles | 0.737 | 3 | 2 | 100% |
| 8 | Hoderlein, S. and H. White (2012) Nonparametric identification in nonseparable panel data models with generalized fixed effects | 0.737 | 3 | 2 | 100% |
| 9 | Melly, B. and G. Santangelo (2015) The changes-in-changes model with covariates | 0.644 | 4 | 1 | 100% |
| 10 | D'Haultfuille, X. and P. Février (2015) Identification of nonseparable triangular models with discrete instruments | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A quantile-based nonadditive fixed effects model | 0.405 | 1 | 1 |