arXiv 22 Sep 2021 · Econometrics · publishedEconometrics Journal (2022) · 11 citations (OpenAlex)
arXiv:2109.10676 · PDF · DOI · OpenAlex · Extracted main text
I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms proposed in Amir-Ahmadi and Drautzburg (2021) to check whether the identified set is nonempty and to sample from the identified set without rejection sampling. I compare the new algorithms to alternatives by applying them to variations of the model considered by Arias et al. (2019), who estimate the effects of US monetary policy using sign and zero restrictions on the monetary policy reaction function. The new algorithms are particularly useful when a rich set of sign restrictions substantially truncates the identified set given the zero 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 | Giacomini, R. and T. Kitagawa (2021) Robust Bayesian Inference for Set-identified Models | 1.000 | 7 | 4 | 100% |
| 2 | Gafarov, B., M. Meier, and J. L. Montiel Olea (2018) Delta-Method Inference for a Class of Set-Identified SVARs | 1.000 | 7 | 3 | 100% |
| 3 | 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 | 0.928 | 4 | 4 | 100% |
| 4 | Giacomini, R., T. Kitagawa, and M. Read (2021) Identification and Inference Under Narrative Restrictions | 0.928 | 4 | 3 | 100% |
| 5 | Uhlig, H (2005) What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure | 0.874 | 6 | 2 | 100% |
| 6 | Rubio-Ramírez, J. F., D. F. Waggoner, and T. Zha (2010) Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference | 0.843 | 3 | 3 | 100% |
| 7 | Amir-Ahmadi, P. and T. Drautzburg (2021) Identification and Inference with Ranking Restrictions | 0.737 | 3 | 2 | 100% |
| 8 | Antolín-Díaz, J. and J. F. Rubio-Ramírez (2018) Narrative Sign Restrictions for SVARs | 0.644 | 2 | 2 | 100% |
| 9 | Arias, J. E., D. Caldara, and J. F. Rubio-Ramírez (2019) The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure | 0.644 | 2 | 2 | 100% |
| 10 | Bacchiocchi, E. and T. Kitagawa (2021) A Note on Global Identification in Structural Vector Autoregressions | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Large Structural VARs with Multiple Sign and Ranking Restrictions | 0.920 | 9 | 4 |
| 2 | Fast Posterior Sampling in Tightly Identified SVARs Using `Soft' Sign Restrictions | 0.843 | 3 | 3 |