Theme

Nonparametric over-identification, efficient learning and testing in GAI era

Time

Wednesday, May 28, 2:00 - 4:00 PM

Location

Guanghua School of Management, PKU

Language

English

Scan for registration

Please await further notification

on the status of your registration.

Abstract

In the rapidly evolving Generative Artificial Intelligence (GAI) era, it is becoming very easy to simulate data and to apply “black-box” machine learning (ML) prediction tools to obtain whatever causal effect/conclusion a researcher likes to report. Many ML tools estimate/learn a nonparametric just-identified relation such as nonparametric conditional mean, nonparametric conditional quantile, nonparametric density in the first stage, which is subsequently used to estimate/learn causal effects or other regular parameters of interest. According to Chen and Santos (2018), the nonparametric (local) over-identification is the necessary condition for the existence of a more efficient estimator and the existence of a non-trivial test for any regular parameters of interest, however. In this talk, I would like to stress the importance of using nonparametric over-identified models in economic causal studies. It turns out that many existing “efficient” casual parameter estimates correspond to the nonparametric just-identified first stage, which means, regardless whatever fancy ML estimators different researchers are using, (i) different ``efficient'' estimators are all first-order equivalent; and (ii) there is no non-trivial test for the validity of the causal relation. In this talk, I will present several works to highlight the usefulness of the notion of nonparametric over-identification in semiparametric policy evaluation/causal inference.

Introduction

Scroll to view

Xiaohong Chen is the Malcolm K. Brachman Professor of Economics, Yale University. Previously Chen has taught at University of Chicago, London School of Economics and New York University. Chen got her PhD in Economics from University of California, San Diego.

Chen is an elected member of the American Academy of Arts and Sciences since 2019, a fellow of the Econometric Society since 2007, a founding fellow of the International Association for Applied Econometrics since 2018, a fellow of the Journal of Econometrics since 2012, and an international fellow of Cemmap since 2007. Chen is a winner of the 2017 China Economics Prize. Chen has been a keynote or an invited speaker in many international conferences. She was the 2018 Sargan Lecturer of the Econometric Society, the 2019 Hilda Geiringer Lecturer, and the 2017 Econometric Theory lecturer.

Chen’s research field is econometrics. She is known for her research in penalized sieve estimation and inference on semiparametric and nonparametric models, such as semiparametric models of nonlinear time series, empirical asset pricing, copula, missing data, measurement error, nonparametric instrumental variables, semi/nonparametric conditional moment restrictions, causal inference.

Chen has published peer-reviewed papers in top-ranked general-purpose journals in economics: Econometrica and Review of Economic Studies; as well as in top-ranked journals in statistics and engineering: Annals of Statistics, Journal of the American Statistical Association, IEEE Tran Information Theory, IEEE Trans Neural Networks.

Chen also published several invited review chapters, including a chapter on the method of sieves in 2007 Handbook of Econometrics volumne 6B. She also won Econometric Theory Multa Scripsit Award in 2012, The Journal of Nonparametric Statistics 2010 Best Paper Award, The Richard Stone Prize in Journal of Applied Econometrics for the years 2008 and 2009, The Arnold Zellner Award for the best theory paper published in Journal of Econometrics in 2006 and 2007. Her PhD thesis was about stochastic approximation/Robbins-Monro procedure in function space for near-epoch dependent processes.

Chen is an editor of Journal of Econometrics since Jan 2019.

Chen was an associate editor of Econometrica, Review of Economic Studies, Quantitative Economics, Journal of Econometrics, Econometric Theory, Journal of Nonparametric Statistics, Econometrics Journal, and others.

Guanghua's 40th Anniversary

Top Scholar Forum

The "Top Scholar Forum" series, launched to celebrate the 40th anniversary of the Guanghua School of Management and promote the high-quality development of academic disciplines, brings together top scholars and experts from around the world. Through lectures and academic exchanges, the forum focuses on frontier issues and key topics in economics and management, employing innovative academic paradigms and scientific methodologies. It encourages interdisciplinary innovation and the exploration of practical challenges, showcasing the latest research and sparking new ideas and perspectives. This international platform for academic dialogue aims to enhance Guanghua’s academic contributions and further elevate its global influence in the fields of economics and management.

來源 |北大光華學術資訊

編輯 |王蒙

近期發布：

Lillian Eby教授蒞臨北大光華頂尖學者學術講堂，深度解析管理學研究中的開放科學

企業人力資源管理：為何做得多卻不見得有效果？