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Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data

Author

Listed:
  • Ji Hyung Lee
  • Yuya Sasaki
  • Alexis Akira Toda
  • Yulong Wang

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.

Suggested Citation

  • Ji Hyung Lee & Yuya Sasaki & Alexis Akira Toda & Yulong Wang, 2022. "Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data," Papers 2204.05480, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2204.05480
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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