Emanuele Bacchiocchi, Toru Kitagawa
arXiv 2 Apr 2025 · Econometrics · 7 citations (OpenAlex)
arXiv:2504.01441 · PDF · DOI · OpenAlex · Extracted main text
This paper analyzes Structural Vector Autoregressions (SVARs) where identification of structural parameters holds locally but not globally. In this case there exists a set of isolated structural parameter points that are observationally equivalent under the imposed restrictions. Although the data do not inform us which observationally equivalent point should be selected, the common frequentist practice is to obtain one as a maximum likelihood estimate and perform impulse response analysis accordingly. For Bayesians, the lack of global identification translates to non-vanishing sensitivity of the posterior to the prior, and the multi-modal likelihood gives rise to computational challenges as posterior sampling algorithms can fail to explore all the modes. This paper overcomes these challenges by proposing novel estimation and inference procedures. We characterize a class of identifying restrictions and circumstances that deliver local but non-global identification, and the resulting number of observationally equivalent parameter values. We propose algorithms to exhaustively compute all admissible structural parameters given reduced-form parameters and utilize them to sample from the multi-modal posterior. In addition, viewing the set of observationally equivalent parameter points as the identified set, we develop Bayesian and frequentist procedures for inference on the corresponding set of impulse responses. An empirical example illustrates our proposal.
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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 | Raffaella Giacomini and Toru Kitagawa (2021) Robust Bayesian Inference for Set-identified Models self | 1.000 | 14 | 4 | 100% |
| 2 | J.E. Arias and J.F. Rubio-Ramírez and D.F Waggoner (2018) Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications | 1.000 | 9 | 4 | 100% |
| 3 | Harald Uhlig (2005) What are the effects of monetary policy on output? Results from an agnostic identification procedure | 1.000 | 8 | 3 | 100% |
| 4 | J. Hamilton and D. Waggoner and T. Zha (2007) Normalization in econometrics | 0.928 | 4 | 3 | 100% |
| 5 | D. Caldara and E. Herbst (2019) Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs | 0.874 | 7 | 2 | 100% |
| 6 | Emanuele Bacchiocchi and Andrea Bastianin and Toru Kitagawa and Elis… (2022) Partially identified heteroskedastic SVARs: Identification and inference self | 0.874 | 5 | 2 | 100% |
| 7 | Rubio-Ramirez, Juan and Waggoner, Dan and Zha, Tao (2010) Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference | 0.874 | 5 | 2 | 100% |
| 8 | Magnus, Jan and Neudecker, Heinz (2007) Matrix differential calculus with applications in statistics and econometrics | 0.843 | 5 | 3 | 60% |
| 9 | Markus Brunnermeier and Darius Palia and Karthik A. Sastry and Chris… Feedbacks: Financial Markets and Economic Activity | 0.843 | 3 | 3 | 100% |
| 10 | Lanne, Markku and Lütkepohl, Helmut Identifying monetary policy shocks via changes in volatility | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 100 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | SVARs with breaks: Identification and inference | 0.958 | 25 | 5 |
| 2 | Identification–aware Markov Chain Monte Carlo | 0.644 | 2 | 2 |
| 3 | On global identification in structural vector autoregressions | 0.405 | 1 | 1 |
| 4 | Inference in Tightly Identified and Large-Scale Sign-Restricted SVARs | 0.405 | 1 | 1 |
| 5 | Partially identified heteroskedastic SVARs | 0.000 | 1 | 1 |