IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/81094.html
   My bibliography  Save this paper

Fully Bayesian Analysis of SVAR Models under Zero and Sign Restrictions

Author

Listed:
  • Kocięcki, Andrzej

Abstract

The paper proposes the methodologically sound method to deal with set identified Structural VAR (SVAR) models under zero and sign restrictions. What distinguishes our method from that proposed by Arias, Rubio-Ramírez and Waggoner (2016) is that we isolated many special cases for which we arrive at more efficient algorithms to draw from the posterior. We illustrate our approach with the help of two serious empirical examples. First of all we challenge the output puzzle found by Uhlig (2005). Second, we check the robustness of the results given by Beaudry et al. (2014) concerning impact of optimism shocks on economy.

Suggested Citation

  • Kocięcki, Andrzej, 2017. "Fully Bayesian Analysis of SVAR Models under Zero and Sign Restrictions," MPRA Paper 81094, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:81094
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/81094/1/MPRA_paper_81094.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    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. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    3. Jonas E. Arias & Juan Rubio-Ramirez & Daniel F. Waggoner, 2013. "Inference Based on SVARs Identied with Sign and Zero Restrictions: Theory and Applications," Working Papers 2013-24, FEDEA.
    4. 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.
    5. Andrew Mountford & Harald Uhlig, 2009. "What are the effects of fiscal policy shocks?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 960-992.
    6. 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.
    7. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    8. 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.
    9. Rubio-Ramírez, Juan Francisco & Caldara, Dario & Arias, Jonas E., 2016. "The Systematic Component of Monetary Policy in SVARs: An Agnostic Identi," CEPR Discussion Papers 11674, C.E.P.R. Discussion Papers.
    10. Eleonora Granziera & Hyungsik Roger Moon & Frank Schorfheide, 2018. "Inference for VARs identified with sign restrictions," Quantitative Economics, Econometric Society, vol. 9(3), pages 1087-1121, November.
    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. 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.
    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. Raffaella Giacomini & Toru Kitagawa, 2021. "Robust Bayesian Inference for Set‐Identified Models," Econometrica, Econometric Society, vol. 89(4), pages 1519-1556, July.
    4. Gan‐Ochir Doojav & Davaasukh Damdinjav, 2023. "The macroeconomic effects of unconventional monetary policies in a commodity‐exporting economy: Evidence from Mongolia," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4627-4654, October.
    5. Giacomini, Raffaella & Kitagawa, Toru & Read, Matthew, 2022. "Robust Bayesian inference in proxy SVARs," Journal of Econometrics, Elsevier, vol. 228(1), pages 107-126.
    6. Pooyan Amir-Ahmadi & Thorsten Drautzburg, 2017. "Identification Through Heterogeneity," Working Papers 17-11, Federal Reserve Bank of Philadelphia.
    7. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2022. "Uncertain identification," Quantitative Economics, Econometric Society, vol. 13(1), pages 95-123, January.
    8. D’Amico, Stefania & King, Thomas B., 2023. "What does anticipated monetary policy do?," Journal of Monetary Economics, Elsevier, vol. 138(C), pages 123-139.
    9. Danne, Christian, 2015. "VARsignR: Estimating VARs using sign restrictions in R," MPRA Paper 68429, University Library of Munich, Germany.
    10. Lutz Kilian, 2013. "Structural vector autoregressions," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 22, pages 515-554, Edward Elgar Publishing.
    11. Pooyan Amir‐Ahmadi & Thorsten Drautzburg, 2021. "Identification and inference with ranking restrictions," Quantitative Economics, Econometric Society, vol. 12(1), pages 1-39, January.
    12. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
    13. IIBOSHI, Hirokuni & IWATA, Yasuharu, 2023. "The Nexus between Public Debt and the Government Spending Multiplier: Fiscal Adjustments Matter," MPRA Paper 116347, University Library of Munich, Germany.
    14. Yasuharu Iwata & Hirokuni IIboshi, 2023. "The Nexus between Public Debt and the Government Spending Multiplier: Fiscal Adjustments Matter," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 830-858, August.
    15. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    16. 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.
    17. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Paper series 20-09, Rimini Centre for Economic Analysis.
    18. 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).
    19. 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.
    20. Victor Pontines, 2021. "The real effects of loan-to-value limits: empirical evidence from Korea," Empirical Economics, Springer, vol. 61(3), pages 1311-1350, September.

    More about this item

    Keywords

    Set identified Structural VAR; Sign restrictions; Monetary policy; Bayesian;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • 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:pra:mprapa:81094. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.