Inference for Moments of Ratios with Robustness against Large Trimming Bias and Unknown Convergence Rate

Friday August 10, 2pm, Carslaw 173

Yuya Sasaki
Vanderbilt University, Department of Economics

Inference for Moments of Ratios with Robustness against Large Trimming Bias and Unknown Convergence Rate

We consider statistical inference for moments of the form E[B/A]. A naive sample mean is unstable with small denominator, A. This paper develops a method of robust inference, and proposes a data-driven practical choice of trimming observations with small A. 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.

Yuya Sasaki is an Associate Professor of Economics at Vanderbilt University. He received his bachelor’s degree and master’s degrees at Utah State University with majors in economics, geography, and mathematics. He received a Ph.D. in economics from Brown University. Yuya Sasaki was an assistant professor of economics at Johns Hopkins University, and then moved to Vanderbilt University as an associate professor. The field of his specialization is econometrics. He is currently an associate editor of Journal of Econometric Methods.