Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak
arXiv 17 Apr 2024 · Econometrics · publishedJournal of Econometrics (2025)
arXiv:2404.11057 · PDF · DOI · OpenAlex · Extracted main text
We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails -- unlike the common centred parameterization. This feature makes it well suited for assessing partial identification of any shock of interest. Second, Monte Carlo experiments on small and large systems indicate that the non-centred setup estimates structural parameters more precisely and normalizes conditional variances efficiently. Third, revisiting prominent fiscal structural vector autoregressions, we show how the non-centred approach identifies tax shocks that are consistent with estimates reported in the literature.
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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 | Chan, J. C. C (2018) Specification tests for time-varying parameter models with stochastic volatility | 1.000 | 10 | 4 | 100% |
| 2 | Blanchard, O. and R. Perotti (2002, November) (2002) An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output | 1.000 | 5 | 3 | 100% |
| 3 | Lütkepohl, H. and T. Woźniak (2020) Bayesian inference for structural vector autoregressions identified by Markov-switching heteroskedasticity | 0.928 | 5 | 4 | 80% |
| 4 | Lewis, D. J (2021) Identifying Shocks via Time-Varying Volatility | 0.894 | 7 | 4 | 71% |
| 5 | Gelfand, A. E. and A. F. M. Smith (1990, jun) (1990) Sampling-Based Approaches to Calculating Marginal Densities | 0.843 | 3 | 3 | 100% |
| 6 | Lütkepohl, H. and G. Milunovich (2016) Testing for identification in SVAR-GARCH models self | 0.811 | 4 | 2 | 100% |
| 7 | Mertens, K. and M. O. Ravn (2014) A reconciliation of SVAR and narrative estimates of tax multipliers | 0.811 | 4 | 2 | 100% |
| 8 | Cadonna, A., S. Frühwirth-Schnatter, and P. Knaus (2020) Triple the gamma unifying shrinkage prior for variance and variable selection in sparse state space and tvp models | 0.737 | 3 | 2 | 100% |
| 9 | Kastner, G. and S. Frühwirth-Schnatter (2014) Ancillarity-sufficiency interweaving strategy (asis) for boosting mcmc estimation of stochastic volatility models | 0.737 | 3 | 2 | 100% |
| 10 | Romer, C. D. and D. H. Romer (2010) The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks | 0.693 | 6 | 1 | 100% |
Showing the top 10 of 59 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification Verification for Structural Vector Autoregressions with Sparse Heterogeneous Markov Switching Heteroskedasticity | 1.000 | 19 | 5 |