  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.
