Sparse Seasonal and Periodic Vector Autoregressive Modeling

• Activity Number: 482
• Type: Topic Contributed
• Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Vladas Pipiras* and Changryong Baek and Richard A. Davis
• Companies: The University of North Carolina at Chapel Hill and Sungkyunkwan University and Columbia University
• Keywords: seasonal vector autoregression ; periodic vector autoregression ; sparsity ; partial spectral coherence ; adaptive lasso ; variable selection

Abstract

Seasonal and periodic vector autoregressions are two common approaches to modeling vector time series exhibiting cyclical variations. The total number of parameters in these models increases rapidly with the dimension and order of the model, making it difficult to interpret the model and questioning the stability of the parameter estimates. To address these and other issues, two methodologies for sparse modeling are presented in this work: first, based on regularization involving adaptive lasso and, second, extending the approach of Davis, Zang and Zheng (2015) for vector autoregressions based on partial spectral coherences. The methods are shown to work well on simulated data, and to perform well on several examples of real vector time series exhibiting cyclical variations.