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RSFAS Seminar | Dr Yanrong Yang

RSFAS Seminar | Dr Yanrong Yang

Speaker: Dr Yanrong Yang

Institution:  ANU

Date and Time: 10:00-11:00am, Thursday, 13 August 2020 

Title: Can we trust PCA on non-stationary Data?

Abstract: This paper establishes asymptotic properties for spiked empirical eigenvalues for high dimensional data with both cross-sectional dependence and dependent sample structure. A new finding from the established theoretical results is that spiked empirical eigenvalues will reflect dependent sample structure instead of cross-sectional structure under some scenarios, which indicates that principal component analysis (PCA) may provide inaccurate inference for cross-sectional structure. An illustrated example is provided to show that some commonly used statistics based on spiked empirical eigenvalues mis-estimate the true number of common factors. As an application on high dimensional time series, we propose a test statistic to distinguish unit root from factor structure, and demonstrate its effective finite sample performance on simulated data. Our results are then applied to analyse OECD health care expenditure data and US mortality data, both of which possess cross-sectional dependence as well as non-stationary temporal dependence. It is worth mentioning that we contribute to statistical justification for the benchmark paper by Lee and Carter (1992) in mortality forecasting.

Updated:   6 August 2020 / Responsible Officer:  CBE Communications and Outreach / Page Contact:  College Web Team