Policy Choice in Time Series by Empirical Welfare Maximization

• Tác giả: Toru Kitagawa, Weining Wang, Mengshan Xu
• Ngôn ngữ: eng
• Ký hiệu phân loại: 330.1556 Systems, schools, theories
• Thông tin xuất bản: 2022
• Bộ sưu tập: Metadata
• ID: 195102

Tóm tắt

This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series. Building on the statistical treatment choice framework, we propose Time-series Empirical Welfare Maximization (T-EWM) methods to estimate an optimal policy rule by maximizing an empirical welfare criterion constructed using nonparametric potential outcome time series. We characterize conditions under which T-EWM consistently learns a policy choice that is optimal in terms of conditional welfare given the time-series history. We derive a nonasymptotic upper bound for conditional welfare regret. To illustrate the implementation and uses of T-EWM, we perform simulation studies and apply the method to estimate optimal restriction rules against Covid-19.