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Filtered Coupling Measures for Variable Selection in Sparse Vector Autoregressive Modeling
필터링된 잔차를 이용한 희박벡터자기회귀모형에서의 변수 선택 측도

Seungkyu Lee, Changryong Baek
2015 Korean Journal of Applied Statistics  
Vector autoregressive (VAR) models in high dimension suffer from noisy estimates, unstable predictions and hard interpretation. Consequently, the sparse vector autoregressive (sVAR) model, which forces many small coefficients in VAR to exactly zero, has been suggested and proven effective for the modeling of high dimensional time series data. This paper studies coupling measures to select non-zero coefficients in sVAR. The basic idea based on the simulation study reveals that removing the
more » ... removing the effect of other variables greatly improves the performance of coupling measures. sVAR model coefficients are asymmetric; therefore, asymmetric coupling measures such as Granger causality improve computational costs. We propose two asymmetric coupling measures, filtered-cross-correlation and filtered-Granger-causality, based on the filtered residuals series. Our proposed coupling measures are proven adequate for heavy-tailed and high order sVAR models in the simulation study.
doi:10.5351/kjas.2015.28.5.871 fatcat:diypp4l2avcf3eldl2obt5szoi