Large-Scale Curve Time Series with Common Stochastic Trends
Prof. Degui LI
Distinguished Professor of Business Economics
Associate Dean (Research and Development)
FBA, UM
Date: 20/01/2026 (Tuesday)
Time: 13:00 to 14:00
Venue: FBA Lobby
Abstract
This paper studies high-dimensional curve time series with common stochastic trends. A dual functional factor model structure is adopted with a high-dimensional factor model for the observed curve time series and a low-dimensional factor model for the latent curves with common trends. A functional PCA technique is applied to estimate the common stochastic trends and functional factor loadings. Under some regularity conditions we derive the mean square convergence and limit distribution theory for the developed estimates, allowing the dimension and sample size to jointly diverge to infinity. We propose an easy-to-implement criterion to consistently select the number of common stochastic trends and further discuss model estimation when the nonstationary factors are cointegrated. Extensive Monte-Carlo simulations and two empirical applications to large-scale temperature curves in Australia and log-price curves of S&P 500 stocks are conducted, showing finite-sample performance and providing practical implementations of the new methodology.
Speaker
Degui’s main research areas include complex time series analysis, functional data analysis, network data modeling, nonparametric and semiparamertric econometrics and high-dimensional econometrics. His research works have been published in leading journals in econometrics, statistics and machine learning such as AoS, ET, JASA, JBES, JoE and JMLR. He was the recipient of ARC Discovery Early Career Researcher Award in 2011 and UK Leverhulme Research Fellowship in 2023 and NSFC Young Scientist Fund (Category A) in 2025. Degui currently serves as the Co-Editor of Econometric Theory and Associate Editor of Journal of Time Series Analysis.
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