Policy Choice in Time Series by Empirical Welfare Maximization

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主讲人	Weining Wang	简介	<p>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.</p>
时间	2022-12-27 (Tuesday) 16:30-18:00	地点	腾讯会议：37586125504
讲座语言	中文	主办单位	中国科学院大学经济与管理学院、中国科学院预测科学研究中心、厦门大学邹至庄经济研究院、NSFC"计量建模与经济政策研究”基础科学中心
承办单位		类型	系列讲座
联系人信息		主持人	Yongmiao Hong
专题网站		专题
主讲人简介	<p>Dr. Weining Wang is a chair professor of financial econometrics in department economics and related studies at the University of York, UK. She received a Doctor Degree in Economics from Humboldt University in Berlin. Her research fields mainly include non-parametric and semi-parametric econometrics, high-dimensional econometrics, network models, time series. He published in several top journals in the areas, including<em> Annals of Statistics, Journal of Business and Economic Statistics, Journal of Econometrics, Journal of the American Statistics Association, Econometric Theory</em>, and others. Her research mainly focuses on panel data, high-dimensional time series models, and other applied econometrics methods. The goal is to address specific economic and financial research questions, such as system risk model analysis, financial derivatives asset pricing, and social network analysis.</p>	期数	“邹至庄讲座”青年学者论坛（第46期）

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