Speaker
Yundong Tu 涂云东
Peking University
Time
Fri., 16:00-17:00, Sept. 13, 2024
Venue
C548, Shuangqing Complex Building A
清华大学双清综合楼A座 C548报告厅
Abstract
The modal factor model represents a new factor model for dimension reduction in high dimensional panel data. Unlike the approximate factor model that targets for the mean factors, it captures factors that influence the conditional mode of the distribution of the observables. Statistical inference is developed with the aid of mode estimation, where the modal factors and the loadings are estimated through maximizing a kernel-type objective function. For practical implementation, an alternating maximization algorithm is designed to obtain the estimators. Two model selection criteria based on information criteria and rank estimation are also proposed to determine the number of factors. The consistency of the proposed estimators and the asymptotic normality of the modal factor estimators are established under some regularity conditions. Simulations demonstrate the nice finite sample performance of our proposed estimators, even in the presence of heavy-tailed and asymmetric idiosyncratic error distributions. Empirical applications illustrate the practical merits of modal factors in forecasting the U.S. inflation rate and real GDP growth.
About the speaker
Yundong Tu 涂云东
Peking University
Yundong Tu is currently a Professor at the Department of Business Statistics and Econometrics, Guanghua School of Management and Center for Statistical Science, Peking University, after receiving his Ph.D. in Economics from University of California, Riverside. His research covers areas such as Econometric Theory, Financial Econometrics and Big Data Analytics. He has published more than 40 papers in the leading economics and statistics journals, including Journal of Econometrics, Econometric Theory, Econometric Reviews, Journal of Business and Economic Statistics, Oxford Bulletin of Economics and Statistics ，Statistica Sinica ，Journal of Empirical Finance, Journal of Management Science and Engineering, Computational Statistics and Data Analysis and so on. His book Time Series Analysis was recently published by Posts & Telecom Press in September 2022.
