Policy choice in time series by empirical welfare maximization

Toru Kitagawa, Weining Wang and Mengshan Xu

No 27/24, CeMMAP working papers from Institute for Fiscal Studies

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: 2024-12-13
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:27/24

DOI: 10.47004/wp.cem.2024.2724

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