The 2020 International Workshop on Quantitative Economics, Jilin University, Lecture 1

Nonparametric Analysis: A Unified Perspective

Lecturer: Yongmiao Hong, Cornell University

Presenter: Jinquan Liu, Jilin University

Location: ZOOM Meeting Room, ID: 9625852673

Time:

2020/8/26 10：00-11：30（UTC+8）

2020/8/25 22：00-23：30（UTC-5）

2020/8/26 03：00-04：30（UTC+0）

2020/8/26 12：00-13：30（UTC+10）

Introduction: As an important methodology in statistics and econometrics, nonparametric analysis has been widely used in empirical studies in economics. In this talk, we will first motivate the importance of nonparametric analysis from an economic perspective, and then develop a unified approach to view various nonparametric smoothing techniques, including series estimation, spline smoothing, and kernel smoothing based on local constant and local polynomials. We also explore the relationships between nonparametric analysis and machine learning.

About the Lecturer: Professor Hong is a fellow of the World Academy of Sciences (TWAS) for the Advancement of Science in Developing Countries, and a fellow of the Econometric Society. He is currently the Ernest S. Liu Professor of Economics and International Studies at Cornell University. Professor Hong's research interests include econometric theory, time series econometrics, financial econometrics and Chinese economy. He publishes refereed articles in mainstream economic, financial and statistical journals such as Annals of Statistics, Biometrika, Econometrica, Journal of American Statistical Association, Journal of Political Economy, Journal of Royal Statistical Society (Series B), Quarterly Journal of Economics, Review of Economic Studies, and Review of Financial Studies. He has been named on Elsevier’s annual “Most Cited Chinese Researchers” list in Economics, Finance and Econometrics from 2014 to 2019.