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

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.
Original languageEnglish (US)
JournalJournal of Econometrics
StateAccepted/In press - 2023

Keywords

  • grouped data
  • income distribution
  • maximum entropy

Fingerprint

Dive into the research topics of 'Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data'. Together they form a unique fingerprint.

Cite this