Policy Choice in Time Series by Empirical Welfare Maximization

Toru Kitagawa; Weining Wang; Mengshan Xu

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arXiv · 2205.03970

Policy Choice in Time Series by Empirical Welfare Maximization

Toru Kitagawa·Weining Wang·Mengshan Xu

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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 unconditional 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.

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BibTeX RIS

Toru Kitagawa, Weining Wang, Mengshan Xu. 2022-05-08. Policy Choice in Time Series by Empirical Welfare Maximization. https://arxiv.org/abs/2205.03970

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