arXiv 11 Apr 2017 · Statistics — Computation · publishedJournal of Forecasting (2020) · 84 citations (OpenAlex)
arXiv:1704.03239 · PDF · DOI · OpenAlex · Extracted main text
We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced-form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation-by-equation estimation. Second, we apply recently developed global-local shrinkage priors to the VAR coefficients to cure the curse of dimensionality. Third, we utilize recent innovations to efficiently sample from high-dimensional multivariate Gaussian distributions. This makes simulation-based fully Bayesian inference feasible when the dimensionality is large but the time series length is moderate. We demonstrate the merits of our approach in an extensive simulation study and apply the model to US macroeconomic data to evaluate its forecasting capabilities.
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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 | Carriero, Andrea, Clark, Todd E., Marcellino, Massimiliano (2016) Common drifting volatility in large Bayesian VARs | 1.000 | 6 | 3 | 100% |
| 2 | Bhattacharya, Anirban, Pati, Debdeep, Pillai, Natesh S., Dunson, Dav… (2015) Dirichlet–Laplace priors for optimal shrinkage | 1.000 | 5 | 3 | 100% |
| 3 | Huber, Florian, Feldkircher, Martin (2019) Adaptive Shrinkage in Bayesian vector autoregressive models self | 0.928 | 4 | 4 | 100% |
| 4 | Koop, Gary, Korobilis, Dimitris, Pettenuzzo, Davide (2019) Bayesian compressed vector autoregressions | 0.928 | 4 | 3 | 100% |
| 5 | Bhattacharya, Anirban, Chakraborty, Antik, Mallick, Bani K (2016) Fast sampling with Gaussian scale mixture priors in high-dimensional regression | 0.843 | 3 | 3 | 100% |
| 6 | Kastner, Gregor, Lopes, Hedibert F (2017) Efficient Bayesian inference for multivariate factor stochastic volatility models self | 0.843 | 3 | 3 | 100% |
| 7 | McCracken, Michael W., Ng, Serena (2016) FRED-MD: A monthly database for macroeconomic research | 0.811 | 4 | 2 | 100% |
| 8 | Giannone, Domenico, Reichlin, Lucrezia (2010) Large Bayesian vector auto regressions | 0.737 | 3 | 2 | 100% |
| 9 | Polson, Nicholas G., Scott, James G., Bernardo, J. M., Bayarri, M. J… (2011) Shrink globally, act locally: Sparse Bayesian regularization and prediction | 0.737 | 3 | 2 | 100% |
| 10 | Stock, James H., Watson, Mark W., Clements, Michael P., Henry, David F (2011) Dynamic factor models | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 47 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 1912.02231 | 1.000 | 9 | 5 |
| 2 | Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends! | 0.935 | 11 | 4 |
| 3 | Dynamic Ordering Learning in Multivariate Forecasting | 0.644 | 2 | 2 |
| 4 | BVARs and Stochastic Volatility | 0.644 | 2 | 2 |
| 5 | Large Bayesian Tensor VARs with Stochastic Volatility | 0.644 | 2 | 2 |
| 6 | Minnesota BART | 0.644 | 2 | 2 |
| 7 | Bayesian nonparametric graphical models for time-varying parameters VAR | 0.511 | 2 | 1 |
| 8 | Bayesian Modeling of TVP-VARs Using Regression Trees | 0.511 | 2 | 2 |
| 9 | Sparse Bayesian Time-Varying Covariance Estimation in Many Dimensions | 0.405 | 1 | 1 |
| 10 | Large Hybrid Time-Varying Parameter VARs | 0.405 | 1 | 1 |