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報告題目：Policy Choice in Time Series by Empirical Welfare Maximization

報告人：王瑋寧約克大學

報告時間：2022年12月27日(周二)，16:30-18:00

騰訊會議ID：375 8612 5504

內容摘要

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.

主講人簡介

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, includingJournal of Business and Economic Statistics,Journal of Econometrics, JournaloftheAmericanStatisticalAssociation, Econometric Theory,Annals of Statisticsand 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.