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http://dx.doi.org/10.7465/jkdi.2014.25.4.807

The sparse vector autoregressive model for PM10 in Korea  

Lee, Wonseok (Department of Statistics, Sungkyunkwan University)
Baek, Changryong (Department of Statistics, Sungkyunkwan University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.4, 2014 , pp. 807-817 More about this Journal
Abstract
This paper considers multivariate time series modelling of PM10 data in Korea collected from 2008 to 2011. We consider both temporal and spatial dependencies of PM10 by applying the sparse vector autoregressive (sVAR) modelling proposed by Davis et al. (2013). It utilizes the partial spectral coherence to measure cross correlation between different regions, in turn provides the sparsity in the model while balancing the parsimony of model and the goodness of fit. It is also shown that sVAR performs better than usual vector autoregressive model (VAR) in forecasting.
Keywords
Long range dependence; partial spectral coherence; PM10; sparse vector autoregressive model; sparsity;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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