Policy Choice in Time Series by Empirical Welfare Maximization

Fuente: arXiv
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Autores principales: Kitagawa, Toru, Wang, Weining, Xu, Mengshan
Formato: Preprint
Publicado: 2022
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author Kitagawa, Toru
Wang, Weining
Xu, Mengshan
author_facet Kitagawa, Toru
Wang, Weining
Xu, Mengshan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2205_03970
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Policy Choice in Time Series by Empirical Welfare Maximization
Kitagawa, Toru
Wang, Weining
Xu, Mengshan
Econometrics
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.
title Policy Choice in Time Series by Empirical Welfare Maximization
topic Econometrics
url https://arxiv.org/abs/2205.03970