SVARs Identification Through Bounds on the Forecast Error Variance

Volpicella, Alessio

doi:10.1080/07350015.2021.1927742

Cited by 9 publications

(3 citation statements)

References 65 publications

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“…However, as in the case where the normalising impulse response is directly bounded away from zero, the identified set obtained under some small lower bound on the FEVD will also be sensitive to the choice of this lower bound when the identified set is unbounded in the absence of this restriction (see Appendix A.5 for an analysis of this case in the context of the bivariate model). Volpicella (2022) proposes imposing bounds on the FEVD, where the bounds are elicited from a range of estimated DSGE models. However, if the assumptions underlying the DSGE models that are used to elicit these bounds lack credibility, the derived bounds on the FEVDs will also lack credibility.…”

Section: Bounds On the Forecast Error Variance Decompositionmentioning

confidence: 99%

The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions

Read¹

2022

RBA Research Discussion Papers

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Structural vector autoregressions that are set identified (e.g. with sign restrictions) are typically used to analyse the effects of standard deviation shocks. However, answering questions of economic interest often requires knowing the effects of a 'unit' shock. For example, central bankers want to answer questions like 'what are the effects of a 100 basis point increase in the policy rate?' The problem is that set-identifying restrictions do not always rule out the possibility that a variable does not react contemporaneously to its own shock. As a consequence, identified sets for the impulse responses to unit shocks may be unbounded, which implies that set-identifying restrictions may be extremely uninformative. Simply assuming that responses are non-zero turns out to be an arbitrary and unsatisfactory solution. I argue that it is therefore important to communicate about the extent to which the identified set may be unbounded, since this tells us about the informativeness of the identifying restrictions, and I develop tools to facilitate this. I explain how to draw useful posterior inferences about impulse responses when identified sets are unbounded with positive probability. I illustrate the empirical relevance of these issues by estimating the response of US output to a 100 basis point federal funds rate shock under different sets of identifying restrictions. Some restrictions are very uninformative about the effects of a 100 basis point shock. The output responses I obtain under a rich set of identifying restrictions lie towards the smaller end of the range of existing estimates.

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Section: Bounds On the Forecast Error Variance Decompositionmentioning

confidence: 99%

The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions

Read¹

2022

RBA Research Discussion Papers

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“…We relate to Giacomini, Kitagawa, and Uhlig (2019) and Giacomini and Kitagawa (2021) in stressing the mapping from reduced form to structural parameters, but we concentrate on a single prior. Others have focused on how to shrink posterior bands associated with the NiWU prior; see, for instance, Antolín‐Díaz and Rubio‐Ramírez (2018), Amir‐Ahmadi and Drautzburg (2021), and Volpicella (2021).…”

Section: Introductionmentioning

confidence: 99%

A new posterior sampler for Bayesian structural vector autoregressive models

Bruns,

Piffer

2023

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We develop an importance sampler for sign restricted Bayesian structural vector autoregressive models. The algorithm nests as a special case the sampler associated with the popular Normal inverse Wishart Uniform prior, while allowing to move beyond such prior in medium sized models. We then propose a prior on contemporaneous impulse responses that provides flexibility on the magnitude and shape of the impact responses. We illustrate the quantitative relevance of the choice of the prior in an application to US monetary policy shocks. We find that the real effects of monetary policy shocks are stronger under our proposed prior than in the Normal inverse Wishart Uniform setup.

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“…Exploiting the insights of their global identification analysis, RWZ also develop efficient and practical algorithms to perform estimation and inference for structural parameters and impulse responses. Their analytical and computational innovations have been instrumental to recent developments in the literature, including set-identified SVARs (Arias et al (2018(Arias et al ( , 2021), Giacomini and Kitagawa (2020), Giacomini et al (2021b), Volpicella (2020), Amir-Ahmadi and Drautzburg ( 2021)), locally-identified SVARs (Bacchiocchi and Kitagawa (2020)), and SVARs with narrative restrictions (Antolín-Díaz and Rubio-Ramírez (2018), Giacomini et al (2021a)), to list a few. RWZ provide several different versions of the necessary and sufficient conditions for global identification.…”

Section: Introductionmentioning

confidence: 99%

A note on global identification in structural vector autoregressions

Bacchiocchi¹,

Kitagawa²

2021

Preprint

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In a landmark contribution to the structural vector autoregression (SVARs) literature, Rubio-Ramírez, Waggoner, and Zha (2010, 'Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,' Review of Economic Studies) shows a necessary and sufficient condition for equality restrictions to globally identify the structural parameters of a SVAR. The simplest form of the necessary and sufficient condition shown in Theorem 7 of Rubio-Ramírez et al (2010) checks the number of zero restrictions and the ranks of particular matrices without requiring knowledge of the true value of the structural or reduced-form parameters. However, this note shows by counterexample that this condition is not sufficient for global identification. Analytical investigation of the counterexample clarifies why their sufficiency claim breaks down.The problem with the rank condition is that it allows for the possibility that restrictions are redundant, in the sense that one or more restrictions may be implied by other restrictions, in which case the implied restriction contains no identifying information. We derive a modified necessary and sufficient condition for SVAR global identification and clarify how it can be assessed in practice.

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SVARs Identification Through Bounds on the Forecast Error Variance

Cited by 9 publications

References 65 publications

The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions

The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions

A new posterior sampler for Bayesian structural vector autoregressive models

A note on global identification in structural vector autoregressions

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