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Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data

Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, Yulong Wang

arXiv 12 Apr 2022 · Econometrics · publishedJournal of Econometrics (2023) · 2 citations (OpenAlex)

arXiv:2204.05480 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a novel nonparametric density estimation method from tabulated summary data based on maximum entropy and prove its strong uniform consistency. Unlike existing kernel-based estimators, our estimator is free from tuning parameters and admits a closed-form density that is convenient for post-estimation analysis. We apply the proposed method to the tabulated summary data of the U.S. tax returns to estimate the income distribution.

Citation extraction

37
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distinct cited
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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
1Miguel Reyes, Mario Francisco-Fernández, and Ricardo Cao (2016) Nonparametric kernel density estimation for general grouped data1.00053100%
2Thomas Piketty and Emmanuel Saez (1913) Income inequality in the United States, 1913–19980.9507386%
3Gordon Blower and Julia E. Kelsall (2002) Nonlinear kernel density estimation for binned data: Convergence in entropy0.87472100%
4José A. Villaseñor and Barry C. Arnold (1989) Elliptical Lorenz curves0.81142100%
5Thomas Piketty (1901) Income inequality in France, 1901–19980.7375340%
6Frank A. Cowell and Fatemeh Mehta (1982) The estimation and interpolation of inequality measures0.73732100%
7Gholamreza Hajargasht, William E. Griffiths, Joseph Brice, D. S. Pra… (2012) Inference for income distributions using grouped data0.73732100%
8Nanak C. Kakwani and Nripesh Podder (1976) Efficient estimation of the Lorenz curve and associated inequality measures from grouped observations0.73732100%
9David W. Scott and Simon J. Sheather (1985) Kernel density estimation with binned data0.73732100%
10Thomas Blanchet, Juliette Fournier, and Thomas Piketty (2022) Generalized Pareto curves: Theory and applications0.64422100%

Showing the top 10 of 37 scored citations.