Tuning parameter-free nonparametric density estimation from tabulated summary data

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

Journal of Econometrics, 2024, vol. 238, issue 1

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.

Keywords: Grouped data; Income distribution; Maximum entropy (search for similar items in EconPapers)
JEL-codes: C14 D31 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:1:s0304407623002841

DOI: 10.1016/j.jeconom.2023.105568

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