Electronic Journal of Statistics

Periodic dynamic factor models: estimation approaches and applications

Changryong Baek, Richard A. Davis, and Vladas Pipiras

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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.

Article information

Electron. J. Statist., Volume 12, Number 2 (2018), 4377-4411.

Received: April 2018
First available in Project Euclid: 18 December 2018

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62H12: Estimation
Secondary: 62H20: Measures of association (correlation, canonical correlation, etc.)

Dynamic factor model periodic vector autoregressive (PVAR) model dimension reduction adaptive lasso

Creative Commons Attribution 4.0 International License.


Baek, Changryong; Davis, Richard A.; Pipiras, Vladas. Periodic dynamic factor models: estimation approaches and applications. Electron. J. Statist. 12 (2018), no. 2, 4377--4411. doi:10.1214/18-EJS1518. https://projecteuclid.org/euclid.ejs/1545123627

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