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- W2343504111 abstract "Abstract This paper considers variable selection in the sparse vector autoregressive (sVAR) model where sparsitycomes from setting small coefficients to exact zeros. In the estimation perspective, Davis et al. (2015)showed that the lasso type of regularization method is successful because it provides a simultaneous variableselection and parameter estimation even for time series data. However, their simulations study reports thatthe regular lasso overestimates the number of non-zero coefficients, hence its finite sample performance needsimprovements. In this article, we show that the adaptive lasso significantly improves the performance wherethe adaptive lasso finds the sparsity patterns superior to the regular lasso. Some tuning parameter selectionsin the adaptive lasso are also discussed from the simulations study.Keywords: sparse vector autoregressive model, adaptive lasso, high dimensional time series 1. 서론 현대의급격한 과학 기술의발전은기존에는 상상할 수조차 없는 다양하고도 대용량의데이터를 생산해 내었다. 본 연구에서는 시간에 따라 관측된 고차원의대용량 시계열 자료를 매우 효과적으로 분석할 수있는 벡터자기상관회귀 모형(vector autoregressive model; VAR)의추정을다룬다. VAR 모형은변수들 사이의종속관계(interdependence)를 고려하여 시간에 따른 종속관계(temporal depen-dence)를 선형 종속관계로 나타내는 모형이다. 보다 구체적으로 먼저 차원이" @default.
- W2343504111 created "2016-06-24" @default.
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- W2343504111 date "2016-02-29" @default.
- W2343504111 modified "2023-09-24" @default.
- W2343504111 title "Adaptive lasso in sparse vector autoregressive models" @default.
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- W2343504111 doi "https://doi.org/10.5351/kjas.2016.29.1.027" @default.
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