Tobias Adrian, Domenico Giannone, Matteo Luciani, Mike West
arXiv 8 May 2025 · Econometrics · publishedFinance and Economics Discussion Series (2025) · 2 citations (OpenAlex)
arXiv:2505.05193 · PDF · DOI · OpenAlex · Extracted main text
We introduce methodology to bridge scenario analysis and model-based risk forecasting, leveraging their respective strengths in policy settings. Our Bayesian framework addresses the fundamental challenge of reconciling judgmental narrative approaches with statistical forecasting. Analysis evaluates explicit measures of concordance of scenarios with a reference forecasting model, delivers Bayesian predictive synthesis of the scenarios to best match that reference, and addresses scenario set incompleteness. This underlies systematic evaluation and integration of risks from different scenarios, and quantifies relative support for scenarios modulo the defined reference forecasts. The framework offers advances in forecasting in policy institutions that supports clear and rigorous communication of evolving risks. We also discuss broader questions of integrating judgmental information with statistical model-based forecasts in the face of unexpected circumstances.
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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 | Tallman, E. and M. West (2022) On entropic tilting and predictive conditioning | 0.928 | 4 | 3 | 100% |
| 2 | Chernis, T., G. Koop, E. Tallman, and M. West (2024) Decision synthesis in monetary policy | 0.843 | 3 | 3 | 100% |
| 3 | Robertson, J. C., E. W. Tallman, and C. H. Whiteman (2005) Forecasting using relative entropy | 0.843 | 3 | 3 | 100% |
| 4 | West, M (2024) Perspectives on constrained forecasting self | 0.843 | 3 | 3 | 100% |
| 5 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth self | 0.737 | 3 | 2 | 100% |
| 6 | Anesti, N., M. Garofalo, S. Lloyd, E. Manuel, and J. Reynolds (2023) Unknown measures: Assessing uncertainty around UK inflation using a new Inflation-at-Risk model | 0.644 | 2 | 2 | 100% |
| 7 | Antolín-Díaz, J., I. Petrella, and J. F. Rubio-Ramírez (2021) Structural scenario analysis with SVARs | 0.644 | 2 | 2 | 100% |
| 8 | Jondeau, E., P. Poncet, and C. Rebillard (2022) Are financial variables useful to complement GDP nowcasting? | 0.644 | 2 | 2 | 100% |
| 9 | Alessandri, P., L. D. Vecchio, and A. Miglietta (2019) Financial conditions and Growth at Risk' in Italy | 0.644 | 2 | 2 | 100% |
| 10 | Figueres, J. M. and M. Jarociński (2020) Vulnerable growth in the Euro area: Measuring the financial conditions | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 70 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2606.16708 | 1.000 | 5 | 3 |
| 2 | FARS: Factor Augmented Regression Scenarios in R | 0.405 | 1 | 1 |
| 3 | Predictive Synthesis under Sporadic Participation: Evidence from Inflation Density Surveys | 0.405 | 1 | 1 |