SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input

Panagiotis Misiakos, Markus Püschel

Keywords: causal inference, DAG learning, time series, structural vector autoregression, sparse input, Laplace distribution, maximum likelihood estimator

TL;DR: We propose SpinSVAR, a maximum likelihood estimation for an SVAR with sparse inputs. SpinSVAR, outperforms existing methods, and effectively identifies structure in financial data.

Abstract:

We introduce SpinSVAR, a novel method for estimating a (linear) structural vector autoregression (SVAR) from time-series data under a sparse input assumption. Unlike prior approaches using Gaussian noise, we model the input as independent and identically distributed (i.i.d.) 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.

Code Link: https://github.com/pmisiakos/SpinSVAR

Readers: auai.org/UAI/2025/Conference, auai.org/UAI/2025/Conference/Area_Chairs, auai.org/UAI/2025/Conference/Reviewers, auai.org/UAI/2025/Conference/Submission406/Authors, auai.org/UAI/2025/Conference/Submission406/Reproducibility_Reviewers

Submission Number: 406