arXiv 17 Mar 2026 · Econometrics
arXiv:2603.16035 · PDF · DOI · OpenAlex · Extracted main text
We propose a structural vector autoregressive model with a new and flexible specification of the volatility process which we call Sparse Heterogeneous Markov-Switching Heteroskedasticity. In this model, the conditional variance of each structural shock changes in time according to its own Markov process. Additionally, it features a sparse representation of Markov processes, in which the number of regimes is set to exceed that of the data-generating process, with some regimes allowed to have zero occurrences throughout the sample. We complement these developments with a definition of a new distribution for normalised conditional variances that facilitates Gibbs sampling and identification verification. In effect, our model: (i) normalises the system and estimates the structural parameters more precisely than popular alternatives; (ii) can be used to verify homoskedasticity reliably and, thus, inform identification through heteroskedasticity; and (iii) features excellent forecasting performance comparable with Stochastic Volatility. Finally, revisiting a prominent macro-financial structural system, we provide evidence for the identification of the US monetary policy shock via heteroskedasticity, with estimates consistent with those reported in the literature.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Brunnermeier, Markus and Palia, Darius and Sastry, Karthik A. and Si… Feedbacks: Financial Markets and Economic Activity | 1.000 | 23 | 5 | 100% |
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| 3 | Sims, Christopher A and Zha, Tao Were There Regime Switches in U.S. Monetary Policy? | 1.000 | 9 | 3 | 100% |
| 4 | Lütkepohl, Helmut and Woźniak, Tomasz Bayesian Inference for Structural Vector Autoregressions Identified by Markov-switching Heteroskedasticity | 1.000 | 8 | 3 | 100% |
| 5 | Lanne, Markku and Lütkepohl, Helmut and Maciejowska, Katarzyna Structural Vector Autoregressions with Markov Switching | 0.874 | 5 | 2 | 100% |
| 6 | Waggoner, Daniel F. and Zha, Tao Likelihood Preserving Normalization in Multiple Equation Models | 0.737 | 5 | 3 | 40% |
| 7 | Rigobon, Roberto Identification Through Heteroskedasticity | 0.737 | 3 | 2 | 100% |
| 8 | Malsiner-Walli, Gertraud and Frühwirth-Schnatter, Sylvia and Grün, B… Model-Based Clustering Based on Sparse Finite Gaussian Mixtures | 0.644 | 2 | 2 | 100% |
| 9 | Normandin, Michel and Phaneuf, Louis Monetary Policy Shocks: Testing Identification Conditions under Time-Varying Conditional Volatility | 0.644 | 2 | 2 | 100% |
| 10 | Tomasz Woźniak (2025) bsvars: Bayesian Estimation of Structural Vector Autoregressive Models | 0.644 | 2 | 2 | 100% |
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