Bootstrap Calibrated Empirical Likelihood Confidence Intervals for Low Income Proportions

摘要

Estimates of proportions of low income individuals are often required in studies of income shares or wealth distributions. Under nonparametric settings, this paper proposes to use the bootstrap calibrated empirical likelihood method to construct confidence intervals for low income proportions. We demonstrate through simulation studies that intervals based on the bootstrap calibrated normal approximation are less satisfactory for samples of small or moderate size while the bootstrap calibrated empirical likelihood ratio confidence intervals perform well for most samples.