  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.
