arXiv 25 Feb 2025 · Econometrics
arXiv:2502.18242 · PDF · DOI · OpenAlex · Extracted main text
We propose a minimum distance estimation approach for quantile panel data models where unit effects may be correlated with covariates. This computationally efficient method involves two stages: first, computing quantile regression within each unit, then applying GMM to the first-stage fitted values. Our estimators apply to (i) classical panel data, tracking units over time, and (ii) grouped data, where individual-level data are available, but treatment varies at the group level. Depending on the exogeneity assumptions, this approach provides quantile analogs of classic panel data estimators, including fixed effects, random effects, between, and Hausman-Taylor estimators. In addition, our method offers improved precision for grouped (instrumental) quantile regression compared to existing estimators. We establish asymptotic properties as the number of units and observations per unit jointly diverge to infinity. Additionally, we introduce an inference procedure that automatically adapts to the potentially unknown convergence rate of the estimator. Monte Carlo simulations demonstrate that our estimator and inference procedure perform well in finite samples, even when the number of observations per unit is moderate. In an empirical application, we examine the impact of the food stamp program on birth weights. We find that the program's introduction increased birth weights predominantly at the lower end of the distribution, highlighting the ability of our method to capture heterogeneous effects across the outcome distribution.
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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 | Chetverikov, D., B. Larsen, and C. Palmer (2016) IV Quantile Regression for Group-Level Treatments, With an Application to the Distributional Effects of Trade | 1.000 | 19 | 6 | 100% |
| 2 | Fernández-Val, I., W. Y. Gao, Y. Liao, and F. Vella (2022) Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes, 1–45 | 1.000 | 8 | 3 | 100% |
| 3 | Galvao, A. F. and L. Wang (2015) Efficient Minimum Distance Estimator for Quantile Regression Fixed Effects Panel Data | 1.000 | 6 | 4 | 100% |
| 4 | Hausman, J. and W. E. Taylor (1981) Panel Data and Unobservable Individual Effects | 0.928 | 4 | 4 | 100% |
| 5 | Hansen, B. E (2022) b): | 0.928 | 4 | 3 | 100% |
| 6 | Galvao, A. F., J. Gu, and S. Volgushev (2020) On the unbiased asymptotic normality of quantile regression with fixed effects | 0.874 | 7 | 2 | 100% |
| 7 | Almond, D., H. W. Hoynes, and D. W. Schanzenbach (2011) Inside the war on poverty: The impact of food stamps on birth outcomes | 0.874 | 5 | 2 | 100% |
| 8 | Chamberlain, G (1994) Quantile Regression, Censoring, and the Structure of Wages | 0.874 | 5 | 2 | 100% |
| 9 | Ahn, S. C. and H. R. Moon (2014) Large-N and Large-T Properties of Panel Data Estimators and the Hausman Test, in | 0.737 | 3 | 2 | 100% |
| 10 | Angrist, J., V. Chernozhukov, and I. Fernández-Val (2006) Quantile Regression under Misspecification, with an Application to the U.S | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | IV regression with distribution-valued outcomes | 0.737 | 3 | 2 |