Emanuele Bacchiocchi, Toru Kitagawa
arXiv 8 May 2024 · Econometrics · 2 citations (OpenAlex)
arXiv:2405.04973 · PDF · DOI · OpenAlex · Extracted main text
In this paper we propose a class of structural vector autoregressions (SVARs) characterized by structural breaks (SVAR-WB). Together with standard restrictions on the parameters and on functions of them, we also consider constraints across the different regimes. Such constraints can be either (a) in the form of stability restrictions, indicating that not all the parameters or impulse responses are subject to structural changes, or (b) in terms of inequalities regarding particular characteristics of the SVAR-WB across the regimes. We show that all these kinds of restrictions provide benefits in terms of identification. We derive conditions for point and set identification of the structural parameters of the SVAR-WB, mixing equality, sign, rank and stability restrictions, as well as constraints on forecast error variances (FEVs). As point identification, when achieved, holds locally but not globally, there will be a set of isolated structural parameters that are observationally equivalent in the parametric space. In this respect, both common frequentist and Bayesian approaches produce unreliable inference as the former focuses on just one of these observationally equivalent points, while for the latter on a non-vanishing sensitivity to the prior. To overcome these issues, we propose alternative approaches for estimation and inference that account for all admissible observationally equivalent structural parameters. Moreover, we develop a pure Bayesian and a robust Bayesian approach for doing inference in set-identified SVAR-WBs. Both the theory of identification and inference are illustrated through a set of examples and an empirical application on the transmission of US monetary policy over the great inflation and great moderation regimes.
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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 | Emanuele Bacchiocchi and Luca Fanelli (2015) Identification in Structural Vector Autoregressive Models with Structural Changes with an Application to U.S. Monetary Policy self | 1.000 | 19 | 6 | 100% |
| 2 | Raffaella Giacomini and Toru Kitagawa (2021) Robust Bayesian Inference for Set-identified Models self | 1.000 | 18 | 3 | 100% |
| 3 | C.A. Sims and T. Zha (2006) Were There Regime Switches in U.S. Monetary Policy? | 1.000 | 8 | 3 | 100% |
| 4 | Markku Lanne and Helmut Lütkepohl (2008) Identifying Monetary Policy Shocks via Changes in Volatility | 1.000 | 7 | 3 | 100% |
| 5 | Harald Uhlig (2005) What are the effects of monetary policy on output? Results from an agnostic identification procedure | 1.000 | 7 | 3 | 100% |
| 6 | Roberto Rigobon (2003) Identification through Heteroskedasticity | 1.000 | 6 | 3 | 100% |
| 7 | E Bacchiocchi and T Kitagawa (2020) Locally- but not globally-identified SVARs self | 0.958 | 25 | 5 | 88% |
| 8 | E. Bacchiocchi and E. Castelnuovo and L. Fanelli (2018) Gimme a Break! Identification and Estimation of the Macroeconomic Effects of Monetary Policy Shocks in the U.S. self | 0.928 | 4 | 3 | 100% |
| 9 | J. Boivin and M. Giannoni (2006) Has Monetary Policy Become More Effective? | 0.874 | 13 | 2 | 100% |
| 10 | 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 | 0.874 | 7 | 2 | 100% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Partially identified heteroskedastic SVARs | 0.405 | 1 | 1 |
| 2 | 2403.08753 | 0.405 | 1 | 1 |
| 3 | Locally- but not Globally-identified SVARs | 0.405 | 1 | 1 |