Emanuele Bacchiocchi, Andrea Bastianin, Toru Kitagawa, Elisabetta Mirto
arXiv 11 Mar 2024 · Econometrics · 7 citations (OpenAlex)
arXiv:2403.06879 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the identification of Structural Vector Autoregressions (SVARs) exploiting a break in the variances of the structural shocks. Point-identification for this class of models relies on an eigen-decomposition involving the covariance matrices of reduced-form errors and requires that all the eigenvalues are distinct. This point-identification, however, fails in the presence of multiplicity of eigenvalues. This occurs in an empirically relevant scenario where, for instance, only a subset of structural shocks had the break in their variances, or where a group of variables shows a variance shift of the same amount. Together with zero or sign restrictions on the structural parameters and impulse responses, we derive the identified sets for impulse responses and show how to compute them. We perform inference on the impulse response functions, building on the robust Bayesian approach developed for set identified SVARs. To illustrate our proposal, we present an empirical example based on the literature on the global crude oil market where the identification is expected to fail due to multiplicity of eigenvalues.
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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 | Markku Lanne and Helmut Lütkepohl (2008) Identifying Monetary Policy Shocks via Changes in Volatility | 1.000 | 6 | 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 | 5 | 4 | 100% |
| 3 | Raffaella Giacomini and Toru Kitagawa (2021) Robust Bayesian Inference for Set-identified Models self | 0.981 | 18 | 5 | 94% |
| 4 | Carriero, Andrea and Marcellino, Massimiliano Giuseppe and Tornese,… (2024) Blended identification in structural VARs | 0.928 | 4 | 3 | 100% |
| 5 | H Lütkepohl and M Meitz and A Net sunajev and P Saikkonen (2021) Testing identification via heteroskedasticity in structural vector autoregressive models | 0.874 | 18 | 7 | 67% |
| 6 | Roberto Rigobon (2003) Identification through Heteroskedasticity | 0.843 | 10 | 5 | 60% |
| 7 | Harald Uhlig (2005) What are the Effects of Monetary Policy? Results from an Agnostic Identification Procedure | 0.843 | 5 | 5 | 60% |
| 8 | Kilian, Lutz (2009) Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market | 0.830 | 7 | 2 | 86% |
| 9 | Juan F. Rubio-Ram\'irez and Daniel F. Waggoner and Tao Zha (2010) Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference | 0.822 | 9 | 5 | 56% |
| 10 | Emanuele Bacchiocchi and Toru Kitagawa (2021) On global identication in Structural Vector Autoregressions self | 0.737 | 4 | 3 | 50% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly | 0.405 | 1 | 1 |