SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input

Panagiotis Misiakos, Markus Püschel
Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, PMLR 286:3048-3092, 2025.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v286-misiakos25a, title = {SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input}, author = {Misiakos, Panagiotis and P\"{u}schel, Markus}, booktitle = {Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence}, pages = {3048--3092}, year = {2025}, editor = {Chiappa, Silvia and Magliacane, Sara}, volume = {286}, series = {Proceedings of Machine Learning Research}, month = {21--25 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v286/main/assets/misiakos25a/misiakos25a.pdf}, url = {https://proceedings.mlr.press/v286/misiakos25a.html}, 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.} }
Endnote
%0 Conference Paper %T SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input %A Panagiotis Misiakos %A Markus Püschel %B Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2025 %E Silvia Chiappa %E Sara Magliacane %F pmlr-v286-misiakos25a %I PMLR %P 3048--3092 %U https://proceedings.mlr.press/v286/misiakos25a.html %V 286 %X 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.
APA
Misiakos, P. & Püschel, M.. (2025). SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 286:3048-3092 Available from https://proceedings.mlr.press/v286/misiakos25a.html.

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