A new algorithm for structural restrictions in Bayesian vector autoregressions

Korobilis, Dimitris

doi:10.1016/j.euroecorev.2022.104241

Cited by 14 publications

(15 citation statements)

References 41 publications

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“…One key observation is that, conditional on the factors q t , the shocks ε t are independent and equation-by-equation estimation is possible. This factor structure has been used in other papers to facilitate estimation of large VARs (Kastner and Huber, 2020;Chan, Forthcoming;Clark et al, Forthcoming) and, in addition, this structure can also facilitate identification of the factors as structural VAR disturbances (Korobilis, 2022;Chan et al, 2022). Moreover, in contrast to VAR-based estimation using a Cholesky decomposition of the error covariances, another convenient feature of our model is that it is invariant to how the variables are ordered in y t (for a formal argument, see, for example, Chan et al, 2022).…”

Section: A Standard Time-varying Parameter Modelmentioning

confidence: 99%

“…In this application, we focus on the question of how adverse business cycle shocks impact a set of inflation measures. To do so, we exploit the factor structure on the reduced-form VAR shocks to identify a business cycle shock (for related identification approaches, see Korobilis, 2022;Chan et al, 2022). As emphasized by Gorodnichenko (2005), in VAR models like ours where the number of variables is relatively large (we have M = 12) having fewer structural shocks than M can facilitate structural interpretation.…”

Section: Capturing Business Cycle Shocksmentioning

confidence: 99%

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Bayesian modeling of time-varying parameters using regression trees

Hauzenberger

Huber

Koop

et al. 2023

Working Paper (Federal Reserve Bank of Cleveland)

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In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.

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Section: A Standard Time-varying Parameter Modelmentioning

confidence: 99%

Section: Capturing Business Cycle Shocksmentioning

confidence: 99%

Bayesian modeling of time-varying parameters using regression trees

Hauzenberger

Huber

Koop

et al. 2023

Working Paper (Federal Reserve Bank of Cleveland)

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“…As is common in macroeconomic analysis using VARs, sign restrictions implied by economic theory are often available to assist structural identification. For a recent contribution linking sign restrictions and factor models, see Korobilis (2020). Below we describe how we can incorporate sign restrictions to achieve point-identification.…”

Section: Order Invariance and Identificationmentioning

confidence: 99%

“…Our paper is related to the recent work by Korobilis (2020), who uses a VAR with a factor error structure for structural analysis. His work is motivated by the computational challenge of imposing a large number of sign restrictions to obtain admissible draws using conventional accept-reject methods (such as the widely used algorithm in Rubio-Ramirez, Waggoner, and Zha, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…This computational hurdle has so far limited the use of sign restrictions to relatively small systems with at most half a dozen dependent variables. 5 Instead of using standard structural VARs, Korobilis (2020) assumes that the factors in his model play the role of structural shocks, and shows that in this case structural analysis can be done efficiently even when one imposes a large number of sign restrictions. His model, however, is homoscedastic, and consequently it is only set-identified.…”

Section: Introductionmentioning

confidence: 99%

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Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis

Chan¹,

Eisenstat²,

Yu³

2022

Preprint

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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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