IDEAS home Printed from https://ideas.repec.org/p/yon/wpaper/2025rwp-245.html

Dissecting Monetary Policy Shocks in Sign-Restricted SVAR Models

Author

Listed:
  • Hyeon-seung Huh

    (Yonsei University)

  • David Kim

    (University of Sydney)

Abstract

The use of sign restrictions to identify monetary policy shocks in structural vector autoregression (SVAR) models has garnered significant attention in recent years. In this context, we revisit two influential studies-Uhlig (2005) and Arias et al. (2019)-which offer conflicting conclusions regarding the output effects of contractionary monetary policy shocks. Our analysis seeks to uncover the underlying causes of these discrepancies and evaluate the sensitivity of the results to alternative model specifications. Specifically, we examine four key factors: (i) the influence of rotation priors on posterior inference in sign-restricted SVAR models, (ii) the robustness of findings when employing an alternative algorithm to generate large sets of responses, (iii) the sensitivity of results to variations in identifying restrictions, and (iv) the robustness of conclusions to changes in the monetary policy equation and the inclusion of the Great Moderation.

Suggested Citation

  • Hyeon-seung Huh & David Kim, 2025. "Dissecting Monetary Policy Shocks in Sign-Restricted SVAR Models," Working papers 2025rwp-245, Yonsei University, Yonsei Economics Research Institute.
  • Handle: RePEc:yon:wpaper:2025rwp-245
    as

    Download full text from publisher

    File URL: http://121.254.254.220/repec/yon/wpaper/2025rwp-245.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    2. Faust, Jon & Swanson, Eric T. & Wright, Jonathan H., 2004. "Identifying VARS based on high frequency futures data," Journal of Monetary Economics, Elsevier, vol. 51(6), pages 1107-1131, September.
    3. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-921, September.
    4. Fisher Lance A. & Huh Hyeon-seung, 2020. "Combining sign and parametric restrictions in SVARs by utilising Givens rotations," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(3), pages 1-19, June.
    5. 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.
    6. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    7. 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.
    8. Eric M. Leeper & Christopher A. Sims & Tao Zha, 1996. "What Does Monetary Policy Do?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(2), pages 1-78.
    9. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    10. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    11. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    12. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, January.
    13. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    14. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    15. Arias, Jonas E. & Caldara, Dario & Rubio-Ramírez, Juan F., 2019. "The systematic component of monetary policy in SVARs: An agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 1-13.
    16. Sam Ouliaris & Adrian Pagan, 2022. "Three Basic Issues that Arise when Using Informational Restrictions in SVARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 1-20, February.
    17. Sam Ouliaris & Adrian Pagan, 2016. "A Method for Working with Sign Restrictions in Structural Equation Modelling," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 605-622, October.
    18. Pooyan Amir Ahmadi & Harald Uhlig, 2015. "Sign Restrictions in Bayesian FaVARs with an Application to Monetary Policy Shocks," NBER Working Papers 21738, National Bureau of Economic Research, Inc.
    19. Barakchian, S. Mahdi & Crowe, Christopher, 2013. "Monetary policy matters: Evidence from new shocks data," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 950-966.
    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. Herwartz, Helmut & Wang, Shu, 2023. "Point estimation in sign-restricted SVARs based on independence criteria with an application to rational bubbles," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    2. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    3. Stefan Schiman-Vukan & Harald Badinger, 2020. "Measuring Monetary Policy with Residual Sign Restrictions at Known Shock Dates," WIFO Working Papers 608, WIFO.
    4. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).
    5. Laumer, Sebastian & Violaris, Andreas-Entony, 2024. "Unconventional monetary policy and policy foresight," Journal of Economic Dynamics and Control, Elsevier, vol. 164(C).
    6. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    7. Rüth, Sebastian K., 2020. "Shifts in monetary policy and exchange rate dynamics: Is Dornbusch's overshooting hypothesis intact, after all?," Journal of International Economics, Elsevier, vol. 126(C).
    8. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    9. Arias, Jonas E. & Caldara, Dario & Rubio-Ramírez, Juan F., 2019. "The systematic component of monetary policy in SVARs: An agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 1-13.
    10. 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.
    11. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    12. Matthew Read, 2022. "The Unit-effect Normalisation in Set-identified Structural Vector Autoregressions," RBA Research Discussion Papers rdp2022-04, Reserve Bank of Australia.
    13. Nguyen, Lam, 2025. "Bayesian inference in proxy SVARs with incomplete identification: Re-evaluating the validity of monetary policy instruments," Journal of Monetary Economics, Elsevier, vol. 155(C).
    14. Fisher, Lance A. & Huh, Hyeon-seung, 2023. "Systematic monetary policy in a SVAR for Australia," Economic Modelling, Elsevier, vol. 128(C).
    15. Dario Caldara & Edward Herbst, 2019. "Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 157-192, January.
    16. Korobilis, Dimitris, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," European Economic Review, Elsevier, vol. 148(C).
    17. Cosmas Dery & Apostolos Serletis, 2021. "Disentangling the Effects of Uncertainty, Monetary Policy and Leverage Shocks on the Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(5), pages 1029-1065, October.
    18. Arefeva, Alina & Arefyev, Nikolay, 2025. "Playing by the Taylor rules or sticking to Friedman’s policy: A new approach to monetary policy identification," Economic Modelling, Elsevier, vol. 143(C).
    19. Samahita Phul, 2025. "Effectiveness of Monetary Policy Transmission in Small Open Economy: An Empirical Exploration on India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 23(2), pages 521-560, June.

    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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:yon:wpaper:2025rwp-245. 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: YERI (email available below). General contact details of provider: https://edirc.repec.org/data/eryonkr.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.