Professor Weining Wang, University of York

'Policy Choice in Time Series by Empirical Welfare Maximization'
Friday 8 April, 3pm - 5pm
Hybrid: Room 305, Gilbert Scott Building (Main Building) and Zoom online seminar

Register at business-events@glasgow.ac.uk

Abstract

This paper develops a novel method to inform policy choice in a dynamic setting where the available data is a single realization of multi-variate time-series. Building on the framework of statistical treatment choice, we propose Time-series Empirical Welfare Maximization (T-EWM) methods that estimate an optimal policy rule in the current period or over multiple periods by maximizing the empirical welfare criterion constructed upon nonparametric potential outcome time-series. We characterize conditions to consistently learn by T-EW Man optimal policy choice in terms of the conditional welfare given the history. We then derive a no asymptotic upper bound of the conditional welfare regret and its minimax lower bound. To illustrate implementation and uses of T-EWM, we perform simulation studies and apply the methods to estimate optimal monetary policy rules with macroeconomic time-series data.

Biography

Weining Wang is a Chair Professor of Financial Econometrics in the Department of 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, and time series. She has published in several top journals in these areas, including Annals of Statistics, Journal of Business and Economic Statistics, Journal of Econometrics, Journal American Statistics Association, Econometric Theory 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.

Further information: business-events@glasgow.ac.uk

First published: 21 March 2022

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