Electronic Journal of Statistics

Periodic dynamic factor models: estimation approaches and applications

Changryong Baek, Richard A. Davis, and Vladas Pipiras

Full-text: Open access

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.

Article information

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

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

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1545123627

Digital Object Identifier
doi:10.1214/18-EJS1518

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

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

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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