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Time-Varying Identification of Structural Vector Autoregressions

Annika Camehl, Tomasz Woźniak

arXiv 27 Feb 2025 · Econometrics

arXiv:2502.19659 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose a novel Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects among alternative patterns of exclusion restrictions to identify structural shocks within the Markov process regimes. We implement the selection through a multinomial prior distribution over these patterns, which is a spike'n'slab prior for individual parameters. By combining a Markov-switching structural matrix with heteroskedastic structural shocks following a stochastic volatility process, the model enables shock identification through time-varying volatility within a regime. As a result, the exclusion restrictions become over-identifying, and their selection is driven by the signal from the data. Our empirical application shows that data support time variation in the US monetary policy shock identification. We also verify that time-varying volatility identifies the monetary policy shock within the regimes.

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Sims, C. A. and T. Zha (2006) Were There Regime Switches in U.S. Monetary Policy?1.00053100%
2Belongia, M. T. and P. N. Ireland (2015) Interest Rates and Money in the Measurement of Monetary Policy0.87452100%
3Leeper, E. M., C. A. Sims, T. Zha, R. E. Hall, and B. S. Bernanke (1996) What Does Monetary Policy Do?0.84333100%
4Baumeister, C. and L. Benati (2013) Unconventional Monetary Policy and the Great Recession: Estimating the Macroeconomic Effects of a Spread Compression at the Zero…0.81142100%
5Feldkircher, M. and F. Huber (2018) Unconventional U.S. Monetary Policy: New Tools, Same Channels?0.81142100%
6Diebold, F. X., G. D. Rudebusch, and S. Boragan Aruoba (2006) The macroeconomy and the yield curve: A dynamic latent factor approach0.81142100%
7Lütkepohl, H., F. Shang, L. Uzeda, and T. Woźniak (2024) Partial identification of heteroskedastic structural vars: Theory and bayesian inference0.73732100%
8Andrés, J., J. D. López-Salido, and J. Vallés (2006, April) (2006) Money in an Estimated Business Cycle Model of the Euro Area0.64422100%
9Chan, J. C. C (2018) Specification tests for time-varying parameter models with stochastic volatility0.64422100%
10Kastner, G. and S. Frühwirth-Schnatter (2014) Ancillarity-sufficiency interweaving strategy (asis) for boosting mcmc estimation of stochastic volatility models0.64422100%

Showing the top 10 of 38 scored citations.