Tuning parameter-free nonparametric density estimation from tabulated summary data

doi:10.1016/j.jeconom.2023.105568

Tuning parameter-free nonparametric density estimation from tabulated summary data

2024-01-01

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DOI：10.1016/j.jeconom.2023.105568

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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

Tuning parameter-free nonparametric density estimation from tabulated summary data

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