Jinyuan Chang - Seminar Series

April 27, 2018 -
2:00pm to 3:00pm

The Seminar Series

Jinyuan Chang, PhD

"Testing for High-Dimensional White Noise Using Maximum Cross-Correlations"

Abstract: We propose a new omnibus test for vector white noise using the maximum absolute autocorrelations and cross-correlations of the component series. Based on an approximation by the L∞-norm of a normal random vector, the critical value of the test can be evaluated by bootstrapping from a multivariate normal distribution. In contrast to the conventional white noise test, the new method is proved to be valid for testing departure from white noise that is not independent and identically distributed. We illustrate the accuracy and the power of the proposed test by simulation, which also shows that the new test outperforms several commonly used methods, including the Lagrange multiplier test and the multivariate Box–Pierce portmanteau tests, especially when the dimension of the time series is high in relation to the sample size. The numerical results also indicate that the performance of the new test can be further enhanced when it is applied to pre-transformed data obtained via the time series principal component analysis proposed by Chang, Guo and Yao (2018, Annals of Statistics, in press). The proposed procedures have been implemented in an R package.


Bio: Jinyuan Chang is a Professor of Statistics and Econometrics at Southwestern University of Finance and Economics, School of Statistics. Professor Chang received his PhD in Statistics from Peking University in 2013. His research interests include high dimensional data analysis, empirical likelihood and its applications, financial econometrics, and functional data analysis.

Location and Address

University of Pittsburgh

Department of Statistics

1811 Wesley W. Posvar Hall

230 S. Bouquet St.

Pittsburgh, PA 15260