Tuning parameter-free nonparametric density estimation from tabulated summary data

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number105568
JournalJournal of Econometrics
Issue number1
StatePublished - Jan 2024


  • Grouped data
  • Income distribution
  • Maximum entropy

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics


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