Toru Kitagawa: Policy Choice in Time-Series by Empirical Welfare Maximization

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Tuesday, October 29, 2024: Toru Kitagawa (Brown University)
Title: Policy Choice in Time-Series by Empirical Welfare Maximization
Discussant: Mikkel Plagborg-Moller (Princeton University)
Abstract: This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate 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 for the current period or over multiple periods 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 then derive a nonasymptotic upper bound for conditional welfare regret and its minimax lower bound. To illustrate the implementation and uses of T-EWM, we perform simulation studies and apply the method to estimate optimal monetary policy rules from macroeconomic time-series data.