Antonio F. Galvao, Jiaying Gu, Stanislav Volgushev
arXiv 31 Jul 2018 · Econometrics · publishedJournal of Econometrics (2020) · 5 citations (OpenAlex)
arXiv:1807.11863 · PDF · DOI · OpenAlex · Extracted main text
Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit, proportional hazard and quantile regression to name just a few) it is impossible to difference out individual effects, and inference is usually justified in a `large n large T' asymptotic framework. However, there is a considerable gap in the type of assumptions that are currently imposed in models with smooth score functions (such as probit, and proportional hazard) and quantile regression. In the present paper we show that this gap can be bridged and establish asymptotic unbiased normality for quantile regression panels under conditions on n,T that are very close to what is typically assumed in standard nonlinear panels. Our results considerably improve upon existing theory and show that quantile regression is applicable to the same type of panel data (in terms of n,T) as other commonly used nonlinear panel data models. Thorough numerical experiments confirm our theoretical findings.
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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 | Galvao and Wang (2015) Efficient Minimum Distance Estimator for Quantile Regression Fixed Effects Panel Data | 1.000 | 11 | 3 | 100% |
| 2 | Kato, Galvao, and Montes-Rojas (2012) Asymptotics for Panel Quantile Regression Models with Individual Effects | 0.774 | 28 | 6 | 46% |
| 3 | Hahn and Kuersteiner (2011) Bias Reduction for Dynamic Nonlinear Panel Models with Fixed Effects | 0.737 | 3 | 2 | 100% |
| 4 | Koenker (2004) Quantile Regression for Longitudinal Data | 0.737 | 3 | 2 | 100% |
| 5 | Volgushev, Chao, and Cheng (2019) Distributed inference for quantile regression processes | 0.693 | 15 | 3 | 33% |
| 6 | Belloni, Chernozhukov, Chetverikov, and Fernández-Val (2017) Conditional Quantile Processes Based on Series or Many Regressors | 0.644 | 4 | 2 | 50% |
| 7 | Galvao and Kato (2016) Smoothed Quantile Regression for Panel Data | 0.644 | 2 | 2 | 100% |
| 8 | Chao, Volgushev, and Cheng (2017) Quantile Processes for Semi and Nonparametric Regression | 0.511 | 10 | 2 | 20% |
| 9 | Ahn and Schmidt (1995) Efficient Estimation of Models for Dynamic Panel Data | 0.405 | 1 | 1 | 100% |
| 10 | Alvarez and Arellano (2003) The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators | 0.405 | 1 | 1 | 100% |
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