SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input

• URL: http://arxiv.org/abs/2501.03130v2
• Date: Fri, 21 Feb 2025 18:04:49 GMT
• Title: SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input
• Authors: Panagiotis Misiakos, Markus Püschel,
• Abstract summary: We introduce SpinSvar, a novel method for estimating a structural vector autoregression from time-series data under sparse input assumption.<n>We model the input as independent Laplacian variables, enforcing sparsity and yielding a maximum likelihood estimator (MLE) based on least absolute error regression.<n>When applied to S&P 500 data, it clusters stocks by sectors and identifies significant structural shocks linked to major price movements.
• Score: 9.548703593014107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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