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Panel Data Quantile Regression with Grouped Fixed Effects

Jiaying Gu, Stanislav Volgushev

arXiv 15 Jan 2018 · Econometrics · publishedJournal of Econometrics (2019) · 55 citations (OpenAlex)

arXiv:1801.05041 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper introduces estimation methods for grouped latent heterogeneity in panel data quantile regression. We assume that the observed individuals come from a heterogeneous population with a finite number of types. The number of types and group membership is not assumed to be known in advance and is estimated by means of a convex optimization problem. We provide conditions under which group membership is estimated consistently and establish asymptotic normality of the resulting estimators. Simulations show that the method works well in finite samples when T is reasonably large. To illustrate the proposed methodology we study the effects of the adoption of Right-to-Carry concealed weapon laws on violent crime rates using panel data of 51 U.S. states from 1977 - 2010.

Citation extraction

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Kato, Galvao, and Montes-Rojas (2012) Asymptotics for Panel Quantile Regression Models with Individual Effects1.00074100%
2Koenker (2004) Quantile Regression for Longitudinal Data1.00074100%
3Koenker (2005) Quantile regression0.92843100%
4Su, Shi, and Phillips (2016) Identifying latent structures in panel data0.84333100%
5Aneja, Donohue, and Zhang (2014) The impact of Right to Carry Laws and the NRC report: the latest lessons from the empirical evaluation of law and policy0.64441100%
6van der Vaart and Wellner (1996) Weak Convergence and Empirical Processes0.64441100%
7Hocking, Vert, Bach, and Joulin (2011) Clusterpath: an Algorithm for Clustering Using Convex Fusion Penalties0.64422100%
8Lamarche (2010) Robust Penalized Quantile Regression Estimation for Panel Data0.64422100%
9Galvao and Wang (2015) Efficient minimum distance estimator for quantile regression fixed effects panel data0.64422100%
10Galvao and Kato (2016) Smoothed quantile regression for panel data0.64422100%

Showing the top 10 of 45 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
12004.051270.73732
2Confidence Set for Group Membership0.40511
3Bootstrap inference for panel data quantile regression0.40511
4A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models0.40511
5Spectral and Post-Spectral Estimators for Grouped Panel Data Models0.40511
6Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure0.40511
7Partitioned Wild Bootstrap for Panel Data Quantile Regression0.40511
8Panel Quantile Regression with Common Shocks0.40511
9Distributional Effects in Censored Quantile Regressions with Endogeneity and Heteroskedasticity0.40511