用稀疏输入假设提升向量自回归模型估计精度与效率
SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input
- 以独立拉普拉斯变量建模输入，通过最小绝对误差实现稀疏性约束
- 在合成数据上准确率与速度均优于现有方法，支持千节点规模计算
- 适用于金融时序分析，可识别股票板块及重大价格冲击事件
我们提出SpinSVAR，一种基于稀疏输入假设的结构化向量自回归（SVAR）估计方法。不同于以往依赖高斯噪声的方法，该方法将输入建模为独立拉普拉斯变量，强制稀疏性，并推导出基于最小绝对误差回归的最大似然估计器（MLE）。在弱假设下，我们提供了MLE的理论一致性保证。SpinSVAR具有高效性：可利用GPU加速，支持数千节点扩展。在拉普拉斯或伯努利-均匀分布的合成数据上，SpinSVAR在准确性和运行时间方面均优于当前最优方法。应用于标普500数据时，能按行业对股票聚类，并识别与重大价格波动相关的显著结构冲击，验证了稀疏输入假设的可行性。
原文摘要 · Abstract (English)
We introduce SpinSVAR, a novel method for estimating a structural vector autoregression (SVAR) from time-series data under sparse input assumption. Unlike prior approaches using Gaussian noise, we model the input as independent Laplacian variables, enforcing sparsity and yielding a maximum likelihood estimator (MLE) based on least absolute error regression. We provide theoretical consistency guarantees for the MLE under mild assumptions. SpinSVAR is efficient: it can leverage GPU acceleration to scale to thousands of nodes. On synthetic data with Laplacian or Bernoulli-uniform inputs, SpinSVAR outperforms state-of-the-art methods in accuracy and runtime. When applied to S&P 500 data, it clusters stocks by sectors and identifies significant structural shocks linked to major price movements, demonstrating the viability of our sparse input assumption.
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