Antonio F. Galvao, Carlos Lamarche, Thomas Parker
arXiv 24 Jul 2025 · Econometrics
arXiv:2507.18494 · PDF · DOI · OpenAlex · Extracted main text
Practical inference procedures for quantile regression models of panel data have been a pervasive concern in empirical work, and can be especially challenging when the panel is observed over many time periods and temporal dependence needs to be taken into account. In this paper, we propose a new bootstrap method that applies random weighting to a partition of the data -- partition-invariant weights are used in the bootstrap data generating process -- to conduct statistical inference for conditional quantiles in panel data that have significant time-series dependence. We demonstrate that the procedure is asymptotically valid for approximating the distribution of the fixed effects quantile regression estimator. The bootstrap procedure offers a viable alternative to existing resampling methods. Simulation studies show numerical evidence that the novel approach has accurate small sample behavior, and an empirical application illustrates its use.
appendix boundary found by appendix_command · 45% of the source is main text. Read the extracted text to check this.
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 | Lamarche and Parker (2023) Wild Bootstrap Inference for Penalized Quantile Regression for Longitudinal Data | 0.961 | 9 | 5 | 89% |
| 2 | Gregory, Lahiri, and Nordman (2018) A Smooth Block Bootstrap for Quantile Regression with Time Series | 0.961 | 9 | 4 | 89% |
| 3 | Kato, Galvao, and Montes-Rojas (2012) Asymptotics for Panel Quantile Regression Models with Individual Effects | 0.956 | 8 | 5 | 88% |
| 4 | Fitzenberger (1998) The Moving Blocks Bootstrap and Robust Inference for Linear Least Squares and Quantile Regressions | 0.956 | 8 | 4 | 88% |
| 5 | Galvao, Gu, and Volgushev (2020) On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects | 0.941 | 6 | 4 | 83% |
| 6 | Shao (2010) Extended Tapered Block Bootstrap | 0.928 | 5 | 4 | 80% |
| 7 | Feng, He, and Hu (2011) Wild Bootstrap for Quantile Regression | 0.928 | 5 | 3 | 80% |
| 8 | Galvao, Parker, and Xiao (2024) Bootstrap Inference for Panel Data Quantile Regression | 0.874 | 6 | 4 | 67% |
| 9 | Goncalves (2011) The Moving Blocks Bootstrap for Panel Linear Regression Models with Individual Fixed Effects | 0.644 | 2 | 2 | 100% |
| 10 | Koenker (2004) Quantile Regression for Longitudinal Data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.