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Tuning parameter-free nonparametric density estimation from tabulated summary data

Author

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

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

  • Lee, Ji Hyung & Sasaki, Yuya & Toda, Alexis Akira & Wang, Yulong, 2024. "Tuning parameter-free nonparametric density estimation from tabulated summary data," Journal of Econometrics, Elsevier, vol. 238(1).
    • Handle: RePEc:eee:econom:v:238:y:2024:i:1:s0304407623002841
    DOI: 10.1016/j.jeconom.2023.105568
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    Keywords

    Grouped data; Income distribution; Maximum entropy;
    All these keywords.

    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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