Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 12, Number 2 (2018), 4377-4411.
Periodic dynamic factor models: estimation approaches and applications
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.
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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Digital Object Identifier
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