Abstract: In this paper, we study high-dimensional curve time series with common stochastic trends. We adopt a dual functional factor model structure 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 time series length to jointly diverge to infinity. We also propose an easy-to-implement criterion to consistently select the number of common stochastic trends and further discuss the model estimation with cointegrated factors. Extensive Monte-Carlo simulation studies and two empirical applications are conducted to illustrate the finite-sample performance of the developed methodology.

报告人简介: 李德柜，现为英国约克大学数学系正教授，主要研究领域包括非参数统计学、时间序列分析、面板数据建模、泛函型数据分析、金融计量经济学、高维计量经济学，并有数十篇论文发表于国际知名统计学和计量经济学刊物如AoS、JASA、JoE、JBES、ET、JMLR等。2011年获澳大利亚科研委员会DECRA奖，2023年获Leverhulme Research Fellowship, 曾受ARC、BA/Leverhulme Trust及Heilbronn Institute等机构的科研资助，现担任《Econometric Theory》、《Journal of Time Series Analysis》及《Econometrics & Statistics》等国际学术刊物的编委。