Generalized principal component analysis for moderately non-stationary vector time series

DOI10.1016/j.jspi.2020.08.007zbMath1460.62143OpenAlexW3048771550MaRDI QIDQ830695

Jiazhu Pan, Fayed Alshammri

Publication date: 7 May 2021

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://strathprints.strath.ac.uk/73541/

zbMATH Keywords

dimension reductionmultivariate time serieseigenanalysisnon-stationary datamoving cross-correlationmoving cross-covariance

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Uses Software

• GTSPCA
• MpermutMax

Cites Work

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• Principal component analysis for second-order stationary vector time series
• Factor modeling for high-dimensional time series: inference for the number of factors
• Covariance regularization by thresholding
• Covariance and precision matrix estimation for high-dimensional time series
• Nonstationary dynamic factor analysis
• Modelling multiple time series via common factors
• Identifying a Simplifying Structure in Time Series
• Testing for Common Trends
• A canonical analysis of multiple time series
• Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
• Forecasting Using Principal Components From a Large Number of Predictors
• Co-Integration and Error Correction: Representation, Estimation, and Testing
• Identifying Cointegration by Eigenanalysis
• Determining the Number of Factors in Approximate Factor Models
• The Generalized Dynamic Factor Model

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