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Policy choice in time series by empirical welfare maximization
- Publication Year :
- 2022
- Publisher :
- London: Centre for Microdata Methods and Practice (cemmap), 2022.
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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 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.
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.od......1687..aa6eaeadb64694d4d30f8b098e8f12f3