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2018 Periodic dynamic factor models: estimation approaches and applications
Changryong Baek, Richard A. Davis, Vladas Pipiras
Electron. J. Statist. 12(2): 4377-4411 (2018). DOI: 10.1214/18-EJS1518

Abstract

A periodic dynamic factor model (PDFM) is introduced as a dynamic factor modeling approach to multivariate time series data exhibiting cyclical behavior and, in particular, periodic dependence structure. In the PDFM, the loading matrices are allowed to depend on the “season” and the factors are assumed to follow a periodic vector autoregressive (PVAR) model. Estimation of the loading matrices and the underlying PVAR model is studied. A simulation study is presented to assess the performance of the introduced estimation procedures, and applications to several real data sets are provided.

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Changryong Baek. Richard A. Davis. Vladas Pipiras. "Periodic dynamic factor models: estimation approaches and applications." Electron. J. Statist. 12 (2) 4377 - 4411, 2018. https://doi.org/10.1214/18-EJS1518

Information

Received: 1 April 2018; Published: 2018
First available in Project Euclid: 18 December 2018

zbMATH: 07003246
MathSciNet: MR3892343
Digital Object Identifier: 10.1214/18-EJS1518

Subjects:
Primary: 62H12, 62M10
Secondary: 62H20

JOURNAL ARTICLE
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Vol.12 • No. 2 • 2018
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