IDEAS home Printed from https://ideas.repec.org/p/bca/bocawp/25-14.html
   My bibliography  Save this paper

Partial Identification of Heteroskedastic Structural Vector Autoregressions: Theory and Bayesian Inference

Author

Listed:
  • Helmut Lütkepohl
  • Fei Shang
  • Luis Uzeda
  • Tomasz Woźniak

Abstract

We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions that ensures the matrix containing structural parameters is either partially or globally unique; (ii) a shrinkage prior distribution for the conditional variance of structural shocks, centred on the hypothesis of homoskedasticity; and (iii) a statistical procedure for assessing the validity of the conditions outlined in (i). Our shrinkage prior ensures that the evidence for identifying a structural shock relies predominantly on the data and is less influenced by the prior distribution. We demonstrate the usefulness of our framework through a fiscal structural model and a series of simulation exercises.

Suggested Citation

  • Helmut Lütkepohl & Fei Shang & Luis Uzeda & Tomasz Woźniak, 2025. "Partial Identification of Heteroskedastic Structural Vector Autoregressions: Theory and Bayesian Inference," Staff Working Papers 25-14, Bank of Canada.
    • Handle: RePEc:bca:bocawp:25-14
    DOI: 10.34989/swp-2025-14
    as

    Download full text from publisher

    File URL: https://doi.org/10.34989/swp-2025-14
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.34989/swp-2025-14?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    2. Darjus Hosszejni & Gregor Kastner, 2019. "Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol," Papers 1906.12123, arXiv.org, revised Feb 2021.
    3. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, Enero-Abr.
    4. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    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. Helmut Lutkepohl & Fei Shang & Luis Uzeda & Tomasz Wo'zniak, 2024. "Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference," Papers 2404.11057, arXiv.org.
    2. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    3. Gulan, Adam & Haavio, Markus & Kilponen, Juha, 2021. "Can large trade shocks cause crises? The case of the Finnish–Soviet trade collapse," Journal of International Economics, Elsevier, vol. 131(C).
    4. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    5. Matthew Read, 2023. "Estimating the Effects of Monetary Policy in Australia Using Sign‐restricted Structural Vector Autoregressions," The Economic Record, The Economic Society of Australia, vol. 99(326), pages 329-358, September.
    6. David S. Jacks & Martin Stuermer, 2021. "Dry bulk shipping and the evolution of maritime transport costs, 1850–2020," Australian Economic History Review, Economic History Society of Australia and New Zealand, vol. 61(2), pages 204-227, July.
    7. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    8. Lutz Kilian & Xiaoqing Zhou, 2020. "Does drawing down the US Strategic Petroleum Reserve help stabilize oil prices?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 673-691, September.
    9. Giovanni Angelini & Luca Fanelli, 2019. "Exogenous uncertainty and the identification of structural vector autoregressions with external instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 951-971, September.
    10. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    11. Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Identification and Inference Under Narrative Restrictions," Papers 2102.06456, arXiv.org.
    12. Atsushi Inoue & Lutz Kilian, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," Working Papers 2030, Federal Reserve Bank of Dallas.
    13. Motto, Roberto & Özen, Kadir, 2022. "Market-stabilization QE," Working Paper Series 2640, European Central Bank.
    14. Sara Boni & Francesco Ravazzolo, 2022. "A Structural Analysis of Unemployment-Generating Supply Shocks with an Application to the US Pharmaceutical Industry," BEMPS - Bozen Economics & Management Paper Series BEMPS94, Faculty of Economics and Management at the Free University of Bozen.
    15. Inoue, Atsushi & Kilian, Lutz, 2022. "Joint Bayesian inference about impulse responses in VAR models," Journal of Econometrics, Elsevier, vol. 231(2), pages 457-476.
    16. Pintor, Gabor, 2016. "The macroeconomic shock with the highest price of risk," LSE Research Online Documents on Economics 86225, London School of Economics and Political Science, LSE Library.
    17. Fabio Canova & Filippo Ferroni, 2022. "Mind the Gap! Stylized Dynamic Facts and Structural Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(4), pages 104-135, October.
    18. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Paper series 20-09, Rimini Centre for Economic Analysis.
    19. Emanuele Bacchiocchi & Andrea Bastianin & Toru Kitagawa & Elisabetta Mirto, 2024. "Partially identified heteroskedastic SVARs," Papers 2403.06879, arXiv.org, revised Mar 2024.
    20. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).

    More about this item

    Keywords

    Econometric and statistical methods; Fiscal policy;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory

    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:bca:bocawp:25-14. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bocgvca.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.