Carlos Lamarche, Thomas Parker
arXiv 10 Apr 2020 · Econometrics · publishedJournal of Econometrics (2023) · 2 citations (OpenAlex)
arXiv:2004.05127 · PDF · DOI · OpenAlex · Extracted main text
The existing theory of penalized quantile regression for longitudinal data has focused primarily on point estimation. In this work, we investigate statistical inference. We propose a wild residual bootstrap procedure and show that it is asymptotically valid for approximating the distribution of the penalized estimator. The model puts no restrictions on individual effects, and the estimator achieves consistency by letting the shrinkage decay in importance asymptotically. The new method is easy to implement and simulation studies show that it has accurate small sample behavior in comparison with existing procedures. Finally, we illustrate the new approach using U.S. Census data to estimate a model that includes more than eighty thousand parameters.
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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 | Wang, Van Keilegom, and Maidman (2018) Wild residual bootstrap inference for penalized quantile regression with heteroscedastic errors | 0.874 | 5 | 2 | 100% |
| 2 | Hagemann (2017) Cluster-Robust Bootstrap Inference in Quantile Regression Models | 0.843 | 4 | 3 | 75% |
| 3 | Knight and Fu (2000) Asymptotics for Lasso-type estimators | 0.843 | 4 | 3 | 75% |
| 4 | Koenker (2004) Quantile Regression for Longitudinal Data | 0.843 | 5 | 3 | 60% |
| 5 | Galvao, Gu, and Volgushev (2020) On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects | 0.822 | 9 | 4 | 56% |
| 6 | Gu and Volgushev (2019) Panel data quantile regression with grouped fixed effects | 0.737 | 3 | 2 | 100% |
| 7 | Harding and Lamarche (2019) A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment | 0.644 | 3 | 2 | 67% |
| 8 | Kock (2016) Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models | 0.644 | 2 | 2 | 100% |
| 9 | Lee, Liao, Seo, and Shin (2018) Oracle Estimation of a Change Point in High-Dimensional Quantile Regression | 0.644 | 2 | 2 | 100% |
| 10 | Wang (2019) $L_1$-regularized Quantile Regression with Many Regressors under Lean Assumptions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Partitioned Wild Bootstrap for Panel Data Quantile Regression | 0.961 | 9 | 5 |
| 2 | Bootstrap inference for panel data quantile regression | 0.511 | 2 | 1 |