Ruey Tsay studies business and economic forecasting, big data analysis, risk modeling and management, credit ratings, and process control. Tsay's research aims at finding the dynamic relationships between variables and how to extract information from messy data. He has authored Analysis of Financial Time Series, 3rd Edition, published in 2010 by Wiley; An Introduction to Analysis of Financial Data with R, published in 2012 by Wiley; Multivariate time series analysis with R and Financial Applications, published in 2014 by Wiley; and coauthored A Course in Time Series Analysis, with D. Pena and G. Tiao, published by Wiley in 2001; Nonlinear Time Series Analysis, with R. Chen, published by Wiley in 2018; and Statistical Learning for Bid Dependent Data, with D. Pena, published by Wiley in 2020. He also published more than 100 articles in leading econometrics and statistical journals. Tsay has worked as a consultant for numerous American, Chinese, and Taiwanese companies. This experience taught him what works in practice and what does not - knowledge that he shares with students in the classroom. He hopes they learn ideas and methods for extracting information from data, large or small.
Tsay is the winner of the 2005 IBM Faculty Research Award and the John Wiley and Sons Author of the Year for his book, Analysis of Financial Time Series, in probability and statistics. He has received nine National Science Foundation grants and holds a U.S. patent for a system and method for building a time series model. He has delivered invited lectures at IMF and central banks of several countries.
He is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, the Royal Statistical Society, and Academia Sinica. He served as coeditor of the Journal of Business & Economic Statistics, Journal of Forecasting, and Statistica Sinica, and as associate editor of several journals.
Tsay earned a bachelor's degree from the National Tsing Hua University in Taiwan in 1974 and a PhD in statistics from the University of Wisconsin-Madison in 1982. He joined Chicago Booth in 1989.
Outside of the classroom, Tsay enjoys gardening.
Modeling high-dimensional time series: A factor model with dynamically dependent factors and diverging eigenvalues (with Z. Gao), Journal of the American Statistical Association, 2021
Testing serial correlation and ARCH effects of high-dimensional time series data (with S. Ling and Y. Yang), Journal of Business and Economic Statistics, 2021
Constrained factor models for high-dimensional matrix-variate time series (with Y. Chen and R. Chen), Journal of the American Statistical Association, 2020
Testing for serial correlations in high-dimensional time series via extreme value theory (Journal of Econometrics, 2020)
Multivariate Time Series Analysis with R and Financial Applications, John Wiley & Sons, 2014. ISBN: 978-1-118-61790-8.
An Introduction to Analysis of Financial Data with R, John Wiley & Sons, 2013. ISBN: 978-0-470-89081-3.
Analysis of Financial Time Series, 3rd Edition, John Wiley & Sons, 2010. ISBN: 978-0-470-41435-4.
Nonlinear Time Series Analysis (with Rong Chen), John Wiley & Sons (2017, to appear)
"Independent component analysis via distance covariance" (with D. Matteson), Journal of the American Statistical Association, (2017), (to appear).
"Modeling structured correlation matrices" (with M. Pourahmadi), Biometrika, (2017), 104, 237-242.
"Principal volatility component analysis" (with discussions), (with Y.P. Hu), Journal of Business & Economic Statistics, (2014).
"Market-based credit ratings" (with D. Creal and B. Gramacy), Journal of Business & Economic Statistics, (2014).
"High dimensional dynamic stochastic copula models" (with D. Creal), Journal of Econometrics, (2015).
"Doubly constrained factor models with applications" (with H. Tsai, C. W. Lin, and C.W. Cheng), Statistica Sinica, (2016).
"Independent component analysis via distance covariance" (with D. Matteson), Journal of the American Statistical Association, (2016).
"Some methods for analyzing big dependent data," Journal of Business & Economic Statistics, (2016).