Policy Choice in Time Series by Empirical Welfare Maximization

Toru Kitagawa, Weining Wang and Mengshan Xu

Papers from arXiv.org

Abstract: 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.

Date: 2022-05, Revised 2024-12
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-mac
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