Sparse Vector Autoregressive Modeling
Sparse Vector Autoregressive Modeling
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DOI：
10.1080/10618600.2015.1092978
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发表时间：
2016-12-01
影响因子：
2.4
通讯作者：
Zheng, Tian
中科院分区：
文献类型：
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作者：
Davis, Richard A.;Zang, Pengfei;Zheng, Tian
向量自回归(VAR)模型被广泛用于多变量时间序列的时间相关性建模。对于较大(甚至是中等)维度，AR系数的数量可能大得令人望而却步，从而导致噪声估计、预测不稳定和难以解释的时间相关性。为了克服这些缺点，我们提出了一种两阶段方法来拟合许多AR系数为零的稀疏VaR(SVaR)模型。第一阶段基于部分谱相干性(PSC)的估计并结合BIC的使用来选择非零AR系数。PSC用于量化多变量过程中边际序列之间的条件关系。然后应用细化第二阶段来进一步减少参数的数量。用仿真和实际数据例子说明了这种两阶段方法的性能。这篇文章的补充材料可以在网上找到。
The vector autoregressive (VAR) model has been widely used for modeling temporal dependence in a multivariate time series. For large (and even moderate) dimensions, the number of the AR coefficients can be prohibitively large, resulting in noisy estimates, unstable predictions, and difficult-to-interpret temporal dependence. To overcome such drawbacks, we propose a two-stage approach for fitting sparse VAR (sVAR) models in which many of the AR coefficients are zero. The first stage selects nonzero AR coefficients based on an estimate of the partial spectral coherence (PSC) together with the use of BIC. The PSC is useful for quantifying the conditional relationship between marginal series in a multivariate process. A refinement second stage is then applied to further reduce the number of parameters. The performance of this two-stage approach is illustrated with simulation and real data examples. Supplementary materials for this article are available online.