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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, Enero-Abr.
    18. 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.
    19. 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.
    20. 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.
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    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

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