Joshua Chan, Eric Eisenstat, Xuewen Yu
arXiv 8 Jul 2022 · Econometrics · 7 citations (OpenAlex)
arXiv:2207.03988 · PDF · DOI · OpenAlex · Extracted main text
Vector autoregressions (VARs) with multivariate stochastic volatility are widely used for structural analysis. Often the structural model identified through economically meaningful restrictions--e.g., sign restrictions--is supposed to be independent of how the dependent variables are ordered. But since the reduced-form model is not order invariant, results from the structural analysis depend on the order of the variables. We consider a VAR based on the factor stochastic volatility that is constructed to be order invariant. We show that the presence of multivariate stochastic volatility allows for statistical identification of the model. We further prove that, with a suitable set of sign restrictions, the corresponding structural model is point-identified. An additional appeal of the proposed approach is that it can easily handle a large number of dependent variables as well as sign restrictions. We demonstrate the methodology through a structural analysis in which we use a 20-variable VAR with sign restrictions to identify 5 structural shocks.
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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 | Korobilis (2020) A new algorithm for structural restrictions in Bayesian vector autoregressions | 1.000 | 5 | 4 | 100% |
| 2 | Chan and Jeliazkov (2009) Efficient simulation and integrated likelihood estimation in state space models self | 0.928 | 5 | 3 | 80% |
| 3 | Furlanetto, Ravazzolo, and Sarferaz (2019) Identification of financial factors in economic fluctuations | 0.874 | 14 | 2 | 100% |
| 4 | Chan, Koop, Poirier, and Tobias (2019) Bayesian Econometric Methods\/ | 0.737 | 3 | 3 | 67% |
| 5 | Sentana and Fiorentini (2001) Identification, estimation and testing of conditionally heteroskedastic factor models | 0.737 | 3 | 2 | 100% |
| 6 | Cogley and Sargent (2005) Drifts and volatilities: Monetary policies and outcomes in the post WWII US | 0.693 | 5 | 1 | 100% |
| 7 | Anderson and Rubin (1956) Statistical inference in factor analysis | 0.644 | 4 | 2 | 50% |
| 8 | Primiceri (2005) Time varying structural vector autoregressions and monetary policy | 0.644 | 4 | 1 | 100% |
| 9 | Carriero, Clark, and Marcellino (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.644 | 2 | 2 | 100% |
| 10 | Hansen and Sargent (1991) Two difficulties in interpreting vector autoregressions | 0.644 | 2 | 2 | 100% |
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