Inference on nonstationarity and common trends in high-dimensional or functional time series

Speaker: Dr Won-Ki Seo

Affiliation: University of Sydney

Online via Zoom: https://uqz.zoom.us/j/83929359619

Abstract 

This paper concerns statistical inference on unit roots and cointegration for time series taking values in a Hilbert space of an arbitrarily large and possibly infinite and/or unknown dimension. When such a time series is given, an important 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. We develop 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. 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 curvevalued (or function-valued) time series, and nonstationary dynamic functional factor models.

About the presenter's meeting 

Dr Seo is willing to have (30-min) 1-to-1 meeting with our colleagues and graduate students on the seminar day. Anyone who wants to schedule a meeting with her, please contact Dr Fu Ouyang to book a time slot.