arXiv 14 Jun 2022 · Econometrics · publishedEuropean Economic Review (2022) · 29 citations (OpenAlex)
arXiv:2206.06892 · PDF · DOI · OpenAlex · Extracted main text
A comprehensive methodology for inference in vector autoregressions (VARs) using sign and other structural restrictions is developed. The reduced-form VAR disturbances are driven by a few common factors and structural identification restrictions can be incorporated in their loadings in the form of parametric restrictions. A Gibbs sampler is derived that allows for reduced-form parameters and structural restrictions to be sampled efficiently in one step. A key benefit of the proposed approach is that it allows for treating parameter estimation and structural inference as a joint problem. An additional benefit is that the methodology can scale to large VARs with multiple shocks, and it can be extended to accommodate non-linearities, asymmetries, and numerous other interesting empirical features. The excellent properties of the new algorithm for inference are explored using synthetic data experiments, and by revisiting the role of financial factors in economic fluctuations using identification based on sign restrictions.
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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 | Rubio-Ramírez, J. F., D. F. Waggoner, and T. Zha (2010) Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference | 0.903 | 19 | 6 | 74% |
| 2 | Baumeister, C. and J. D. Hamilton (2015) Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information | 0.874 | 6 | 3 | 67% |
| 3 | Koop, G. and D. Korobilis (2010) Bayesian Multivariate Time Series Methods for Empirical Macroeconomics | 0.843 | 3 | 3 | 100% |
| 4 | Kilian, L. and H. Lütkepohl (2017) Structural Vector Autoregressive Analysis | 0.811 | 4 | 2 | 100% |
| 5 | Furlanetto, F., F. Ravazzolo, and S. Sarferaz (2019) Identification of Financial Factors in Economic Fluctuations | 0.800 | 39 | 5 | 51% |
| 6 | Stock, J. H. and M. W. Watson (2005) Implications of Dynamic Factor Models for VAR Analysis, Working Paper 11467, National Bureau of Economic Research | 0.737 | 3 | 2 | 100% |
| 7 | Ahmadi, P. A. and H. Uhlig (2015) Sign Restrictions in Bayesian FaVARs with an Application to Monetary Policy Shocks, Working Paper 21738, National Bureau of Econ… | 0.644 | 4 | 1 | 100% |
| 8 | Bernanke, B. S., J. Boivin, and P. Eliasz (2005) Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach* | 0.644 | 2 | 2 | 100% |
| 9 | Geweke, J. F (1996) Bayesian Inference for Linear Models Subject to Linear Inequality Constraints, in | 0.630 | 8 | 2 | 38% |
| 10 | Arias, J. E., J. F. Rubio-Ramírez, and D. F. Waggoner (2018) Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications | 0.585 | 10 | 3 | 20% |
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