IDEAS home Printed from https://ideas.repec.org/p/boe/boeewp/023287.html

Structural forecast analysis

Author

Listed:
  • Davide Brignone

    (Bank of England)

  • Michele Piffer

    (Bank of England)

Abstract

This paper shows how the structural representation of a vector autoregressive (VAR) model can support forecast analysis. We offer a unified framework that formalises how the structural form of the model can help form a narrative for two key statistics in real-time VAR forecasting: the forecast errors relative to the outturn of the data, and the consequent revisions of the forecast. To illustrate the method developed, we conduct a stylised real-time exercise on the UK, focusing on the inflation surge that followed the pandemic. We show that the inflation forecast produced by a four-variable VAR model was revised upwards not only due to contractionary supply-side shocks, but also due to a mix of expansionary demand-side shocks, and a revision in the past shocks.

Suggested Citation

  • Davide Brignone & Michele Piffer, 2026. "Structural forecast analysis," Bank of England Staff Working Paper series 1165, Bank of England.
    • Handle: RePEc:boe:boeewp:023287
    as

    Download full text from publisher

    File URL: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2026/structural-forecast-analysis.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Atsushi Inoue & Lutz Kilian, 2026. "When Is the Use of Gaussian-Inverse Wishart-Haar Priors Appropriate?," Journal of Political Economy, University of Chicago Press, vol. 134(2), pages 773-794.
    2. Atsushi Inoue & Lutz Kilian, 2026. "The Conventional Impulse Response Prior in VAR Models With Sign Restrictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(3), pages 310-322, April.
    3. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    4. Régis Barnichon & Geert Mesters, 2023. "A Sufficient Statistics Approach for Macro Policy," American Economic Review, American Economic Association, vol. 113(11), pages 2809-2845, November.
    5. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    6. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, June.
    7. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    8. Martin Bruns & Michele Piffer, 2023. "A new posterior sampler for Bayesian structural vector autoregressive models," Quantitative Economics, Econometric Society, vol. 14(4), pages 1221-1250, November.
    9. Giannone, Domenico & Primiceri, Giorgio, 2024. "The drivers of post-pandemic inflation," CEPR Discussion Papers 19377, Centre for Economic Policy Research.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naoya Nagasaka, 2026. "Identifying Macro Shocks from Micro Evidence: A Mixed Autoregressive Approach," Discussion Paper Series DP2026-18, Research Institute for Economics & Business Administration, Kobe University.
    2. Bernardino Adão & Sandra Gomes & Laura Alpizar, 2025. "On how to assess the impact of monetary policy," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    3. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    4. Robin Braun & Ralf Brüggemann, 2020. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2020-01, Department of Economics, University of Konstanz.
    5. Hoang, Nam & Grieb, Terrance, "undated". "Hedging Positions, Basis, and Futures Risk Premium: A Disaggregated Data Analysis on US Wheat Markets," 2018 Annual Meeting, August 5-7, Washington, D.C. 273799, Agricultural and Applied Economics Association.
    6. Terrance Grieb & Nam Hoang, 2019. "The Effects of Hedging and Speculation on Cash-Futures Basis: Results from U.S. Wheat Markets," Review of Economics & Finance, Better Advances Press, Canada, vol. 17, pages 1-15, August.
    7. Dominik Bertsche & Robin Braun, 2022. "Identification of Structural Vector Autoregressions by Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 328-341, January.
    8. Nocera, Andrea & Roma, Moreno, 2017. "House prices and monetary policy in the euro area: evidence from structural VARs," Working Paper Series 2073, European Central Bank.
    9. Mikhail Mamonov & Anna Pestova, 2021. ""Sorry, You're Blocked." Economic Effects of Financial Sanctions on the Russian Economy," CERGE-EI Working Papers wp704, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    10. Anastasios Evgenidis & Anastasios G. Malliaris, 2022. "Monetary policy, financial shocks and economic activity," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 429-456, August.
    11. Pestova, Anna (Пестова, Анна) & Mamonov, Mikhail (Мамонов, Михаил), 2016. "Estimating the Influence of Different Shocks on Macroeconomic Indicators and Developing Conditional Forecasts on the Basis of BVAR Model for the Russian Economy [Оценка Влияния Различных Шоков На Динамику Макроэкономических Показателей В России И ," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 4, pages 56-92, August.
    12. repec:spo:wpmain:info:hdl:2441/27od5pb99881folvtfs8s3k16l is not listed on IDEAS
    13. Deskar-Škrbić, Milan & Kotarac, Karlo & Kunovac, Davor, 2020. "The third round of euro area enlargement: Are the candidates ready?," Journal of International Money and Finance, Elsevier, vol. 107(C).
    14. Kim, Soohyeon & Kim, Jihyo & Heo, Eunnyeong, 2021. "Speculative incentives to hoard aluminum: Relationship between capital gains and inventories," Resources Policy, Elsevier, vol. 70(C).
    15. Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
    16. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..
    17. Martin Bruns & Michele Piffer, 2018. "Bayesian Structural VAR models: a new approach for prior beliefs on impulse responses," Working Papers 878, Queen Mary University of London, School of Economics and Finance.
    18. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Monti, Francesca & Sokol, Andrej, 2022. "Nowcasting with large Bayesian vector autoregressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 500-519.
    19. Mori, Lorenzo, 2026. "Fiscal shocks and the surge of inflation," European Economic Review, Elsevier, vol. 187(C).
    20. Matthew Read & Dan Zhu, 2025. "Fast Posterior Sampling in Tightly Identified SVARs Using 'Soft' Sign Restrictions," RBA Research Discussion Papers rdp2025-03, Reserve Bank of Australia.
    21. Endong Wang, 2024. "Local projections identify the same policy counterfactuals as empirical and structural models," Papers 2409.09577, arXiv.org, revised Feb 2026.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boe:boeewp:023287. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Research (email available below). General contact details of provider: https://edirc.repec.org/data/boegvuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.