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 multivariate time series. Overcoming challenges unique to time-series setting such as time-varying environments, history-dependent welfare, dynamic causal effects, and statistical dependence, we propose Time-series Empirical Welfare Maximization (T-EWM) methods. We characterize conditions for T-EWM to consistently learn optimal policies conditional or unconditinal on the time-series history, and derive nonasymptotic upper bounds for the welfare regrets. We illustrate a use of T-EWM for optimal restriction rules against Covid-19.

Date: 2022-05, Revised 2025-11
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-mac
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Citations: View citations in EconPapers (2)

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