2021/07/04 by Aramayis Dallakyan, Dallakyan, Aramayis, Rakheon Kim +3
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Grey System Theory Applications #Monetary Policy and Economic Impact #stat.CO
paper · pdf · doi:10.48550/arxiv.2107.01659
arxiv created 2021/07/04 · openalex publication_date 2021/07/04 · arxiv updated 2021/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We improve upon the two-stage sparse vector autoregression (sVAR) method in Davis et al. (2016) by proposing an alternative two-stage modified sVAR method which relies on time series graphical lasso to estimate sparse inverse spectral density in the first stage, and the second stage refines non-zero entries of the AR coefficient matrices using a false discovery rate (FDR) procedure. Our method has the advantage of avoiding the inversion of the spectral density matrix but has to deal with optimization over Hermitian matrices with complex-valued entries. It significantly improves the computational time with a little loss in forecasting performance. We study the properties of our proposed method and compare the performance of the two methods using simulated and a real macro-economic dataset. Our simulation results show that the proposed modification or msVAR is a preferred choice when the goal is to learn the structure of the AR coefficient matrices while sVAR outperforms msVAR when the ultimate task is forecasting.