Title: Inference for moments of ratios with robustness against large trimming bias and unknown convergence rate
Authors: Yuya Sasaki - Vanderbilt University (United States) [presenting]
Takuya Ura - Department of Economics (United States)
Abstract: Statistical inference for moments of the form $E[B/A]$ is considered. A naive sample mean is unstable with small denominator $A$. A method of robust inference is developed, and a data-driven practical choice of trimming observations with small A is proposed. Our sense of the robustness is twofold. First, bias correction allows for robustness against large trimming bias. Second, adaptive inference allows for robustness against unknown convergence rate. The proposed method allows for closer-to-optimal trimming, and more informative inference results in practice. This practical advantage is demonstrated for inverse propensity score weighting through simulation studies and real data analysis.