High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood

Kimmo Suotsalo*, Yingying Xu, Jukka Corander, Johan Pensar

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically assume a fully connected model which provides no direct insight to the model structure and may lead to highly noisy estimates of the parameters. Because of these limitations, there has been an increasing interest towards methods that produce sparse estimates through penalized regression. However, such methods are computationally intensive and may become prohibitively time-consuming when the number of variables in the model increases. In this paper we adopt an approximate Bayesian approach to the learning problem by combining fractional marginal likelihood and pseudo-likelihood. We propose a novel method, PLVAR, that is both faster and produces more accurate estimates than the state-of-the-art methods based on penalized regression. We prove the consistency of the PLVAR estimator and demonstrate the attractive performance of the method on both simulated and real-world data.

Original languageEnglish
Article number73
Number of pages18
Issue number6
Publication statusPublished - Nov 2021
MoE publication typeA1 Journal article-refereed


  • Fractional marginal likelihood
  • Gaussian graphical models
  • Multivariate time series
  • Pseudo-likelihood
  • Vector autoregression


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