Combining shrinkage and sparsity in conjugate vector autoregressive models

@article{Hauzenberger2021CombiningSA,
  title={Combining shrinkage and sparsity in conjugate vector autoregressive models},
  author={Niko Hauzenberger and Florian Huber and Luca Onorante},
  journal={Journal of Applied Econometrics (Chichester, England)},
  year={2021},
  volume={36},
  pages={304 - 327}
}
Summary Conjugate priors allow for fast inference in large dimensional vector autoregressive (VAR) models. But at the same time, they introduce the restriction that each equation features the same set of explanatory variables. This paper proposes a straightforward means of postprocessing posterior estimates of a conjugate Bayesian VAR to effectively perform equation‐specific covariate selection. Compared with existing techniques using shrinkage alone, our approach combines shrinkage and… 
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