🤖 AI Summary
Dynamic multivariate time series pose challenges including time-varying environments, historical dependence, dynamic causal effects, and statistical dependencies.
Method: This paper proposes Time-series Empirical Welfare Maximization (T-EWM), the first extension of the empirical welfare maximization framework to dynamic time-series settings. T-EWM employs nonparametric potential outcome modeling and conditional welfare optimization to learn dynamic optimal policies.
Contribution/Results: We establish theoretical guarantees, including conditional welfare consistency and a non-asymptotic upper bound on policy regret. In simulation studies and an empirical application to COVID-19 containment policy evaluation, T-EWM significantly improves policy welfare and achieves rapid regret convergence under limited samples. The framework provides a novel paradigm for dynamic decision-making that balances statistical rigor with practical feasibility.
📝 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 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.
