论文标题
经验福利最大化的时间序列政策选择
Policy Choice in Time Series by Empirical Welfare Maximization
论文作者
论文摘要
本文在动态设置中开发了一种新的策略选择方法，其中可用数据是多变量时间序列。在统计治疗选择框架的基础上，我们提出了时间序列的经验福利最大化（T-EWM）方法，以通过最大化使用非参数潜在结果时间序列构建的经验福利标准来估算最佳政策规则。我们表征了T-EWM始终学习的条件，鉴于时间序列的历史记录，它在条件福利方面是最佳选择的条件。我们为有条件的福利遗憾得出了一个非杂种上限。为了说明T-EWM的实施和用途，我们执行仿真研究，并将该方法应用于估计COVID-19的最佳限制规则。
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.
