Niko Hauzenberger, Florian Huber, Luca Onorante
arXiv 20 Feb 2020 · Econometrics · publishedJournal of Applied Econometrics (2020)
arXiv:2002.08760 · PDF · DOI · OpenAlex · Extracted main text
Conjugate priors allow for fast inference in large dimensional vector autoregressive (VAR) models but, at the same time, introduce the restriction that each equation features the same set of explanatory variables. This paper proposes a straightforward means of post-processing posterior estimates of a conjugate Bayesian VAR to effectively perform equation-specific covariate selection. Compared to existing techniques using shrinkage alone, our approach combines shrinkage and sparsity in both the VAR coefficients and the error variance-covariance matrices, greatly reducing estimation uncertainty in large dimensions while maintaining computational tractability. We illustrate our approach by means of two applications. The first application uses synthetic data to investigate the properties of the model across different data-generating processes, the second application analyzes the predictive gains from sparsification in a forecasting exercise for US data.
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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 | Ray P and Bhattacharya A (2018) Signal Adaptive Variable Selector fo… arXiv preprint arXiv:1810.09004 | 1.000 | 10 | 4 | 100% |
| 2 | Koop GM (2013) Forecasting with Medium and Large Bayesian VARs Journal of Applied Econometrics 28(2), 177–203 | 1.000 | 9 | 4 | 100% |
| 3 | Bańbura M, Giannone D and Reichlin L (2010) Large Bayesian vector au… Journal of Applied Econometrics 25(1), 71–92 | 1.000 | 6 | 3 | 100% |
| 4 | Hahn PR and Carvalho CM (2015) Decoupling Shrinkage and Selection in… Journal of the American Statistical Association 110(509), 435–448 | 1.000 | 6 | 3 | 100% |
| 5 | Huber F, Koop G and Onorante L (2020) Inducing Sparsity and Shrinkag… Journal of Business & Economic Statistics 0(just-accepted) | 0.874 | 5 | 2 | 100% |
| 6 | McCracken MW and Ng S (2016) FRED-MD: A Monthly Database for Macroec… Journal of Business & Economic Statistics 34(4), 574–589 | 0.843 | 4 | 3 | 75% |
| 7 | Giannone D, Lenza M and Primiceri GE (2015) Prior selection for vect… Review of Economics and Statistics 97(2), 436–451 | 0.737 | 3 | 2 | 100% |
| 8 | Clark TE (2011) Real-time density forecasts from Bayesian vector aut… Journal of Business & Economic Statistics 29(3), 327–341 | 0.693 | 6 | 1 | 100% |
| 9 | Hansen PR, Lunde A and Nason JM (2011) The model confidence set Econometrica 79(2), 453–497 | 0.693 | 6 | 1 | 100% |
| 10 | Carriero A, Clark TE and Marcellino M (2019) Large Bayesian vector a… Journal of Econometrics 212(1), 137–154 | 0.644 | 2 | 2 | 100% |
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