Florian Huber, Gary Koop, Massimiliano Marcellino, Tobias Scheckel
arXiv 23 Jul 2024 · Econometrics
arXiv:2407.16349 · PDF · Extracted main text
Commonly used priors for Vector Autoregressions (VARs) induce shrinkage on the autoregressive coefficients. Introducing shrinkage on the error covariance matrix is sometimes done but, in the vast majority of cases, without considering the network structure of the shocks and by placing the prior on the lower Cholesky factor of the precision matrix. In this paper, we propose a prior on the VAR error precision matrix directly. Our prior, which resembles a standard spike and slab prior, models variable inclusion probabilities through a stochastic block model that clusters shocks into groups. Within groups, the probability of having relations across group members is higher (inducing less sparsity) whereas relations across groups imply a lower probability that members of each group are conditionally related. We show in simulations that our approach recovers the true network structure well. Using a US macroeconomic data set, we illustrate how our approach can be used to cluster shocks together and that this feature leads to improved density forecasts.
appendix boundary found by appendix_command · 90% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Sirio Legramanti, Tommaso Rigon, Daniele Durante, and David B. Dunson (2022) Extended stochastic block models with application to criminal networks | 0.965 | 10 | 3 | 90% |
| 2 | Krzysztof Nowicki and Tom A. B Snijders (2001) Estimation and prediction for stochastic block structures | 0.874 | 5 | 2 | 100% |
| 3 | Andrea Carriero, Joshua Chan, Todd E. Clark, and Massimiliano Marcel… (2021) Corrigendum to “large bayesian vector autoregressions with stochastic volatility and non-conjugate priors” [j. econometrics 212… self | 0.737 | 3 | 3 | 67% |
| 4 | Jonas E. Arias, Juan F. Rubio-Ramírez, and Minchul Shin (2022) Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models | 0.737 | 3 | 2 | 100% |
| 5 | Paul W. Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt (1983) Stochastic blockmodels: First steps | 0.737 | 3 | 2 | 100% |
| 6 | Hemant Ishwaran and J. Sunil Rao (2005) Spike and slab variable selection: Frequentist and Bayesian strategies | 0.644 | 3 | 2 | 67% |
| 7 | Daniel Felix Ahelegbey, Monica Billio, and Roberto Casarin (2016) Sparse graphical vector autoregression: A bayesian approach | 0.644 | 2 | 2 | 100% |
| 8 | Daniel Felix Ahelegbey, Monica Billio, and Roberto Casarin (2016) Bayesian graphical nodels for structural vector autoregressive processes | 0.644 | 2 | 2 | 100% |
| 9 | Monica Billio, Roberto Casarin, and Luca Rossini (2019) Bayesian nonparametric sparse var models | 0.644 | 2 | 2 | 100% |
| 10 | Edward I. George, Dongchu Sun, and Shawn Ni (2015) Bayesian stochastic search for var model restrictions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 50 scored citations.