A0213
Title: Inference on nonstationarity and common stochastic trends in high-dimensional or functional time series
Authors: Won-Ki Seo - University of Sydney (Australia) [presenting]
Morten Nielsen - Queen's University (Canada)
Dakyung Seong - University of Sydney (Australia)
Abstract: Statistical inference concerns unit roots and cointegration for time series taking values in a Hilbert space of an arbitrarily large, possibly infinite, and/or unknown dimension. When such a time series is given, an essential first step is to estimate the number of stochastic trends, which indicates how many linearly independent unit root processes are embedded in the time series. Statistical inference on the number of stochastic trends that remains asymptotically valid even when the time series of interest takes values in a Hilbert space of an arbitrary and indefinite dimension is developed. This has wide applicability in practice; for example, in the case of cointegrated vector time series of finite dimension, in a high-dimensional factor model that includes a finite number of nonstationary factors, in the case of cointegrated curve-valued (or function-valued) time series, and nonstationary dynamic functional factor models.
