IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp1563.html
   My bibliography  Save this paper

Assessing Identifying Restrictions in SVAR Models

Author

Listed:
  • Michele Piffer

Abstract

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a prior distribution that covers the parameter space both where the restrictions are satisfied and where they are not satisfied. I show that the data determine whether the probability mass in favour of the restrictions increases or not from prior to posterior. Using two applications, I find support for the restrictions used by Baumeister & Hamilton (2015a) in their two-equation model of labor demand and supply, and I find support for the true data generating process in a simulation exercise on the New Keynesian model.

Suggested Citation

  • Michele Piffer, 2016. "Assessing Identifying Restrictions in SVAR Models," Discussion Papers of DIW Berlin 1563, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1563
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.529759.de/dp1563.pdf
    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. Koop, Gary & Poirier, Dale J., 1997. "Learning about the across-regime correlation in switching regression models," Journal of Econometrics, Elsevier, vol. 78(2), pages 217-227, June.
    3. Paustian Matthias, 2007. "Assessing Sign Restrictions," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-33, August.
    4. Mr. Roland Straub & Gert Peersman, 2006. "Putting the New Keynesian Model to a Test," IMF Working Papers 2006/135, International Monetary Fund.
    5. Faust, Jon & Leeper, Eric M, 1997. "When Do Long-Run Identifying Restrictions Give Reliable Results?," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 345-353, July.
    6. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    7. Kociecki, Andrzej, 2013. "Bayesian Approach and Identification," MPRA Paper 46538, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Markku Lanne & Jani Luoto, 2016. "Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression," CREATES Research Papers 2016-04, Department of Economics and Business Economics, Aarhus University.

    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. Canova, Fabio & Paustian, Matthias, 2011. "Business cycle measurement with some theory," Journal of Monetary Economics, Elsevier, vol. 58(4), pages 345-361.
    2. Schenkelberg, Heike & Watzka, Sebastian, 2013. "Real effects of quantitative easing at the zero lower bound: Structural VAR-based evidence from Japan," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 327-357.
    3. Renée Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-960, December.
    4. Francesco Furlanetto & Francesco Ravazzolo & Samad Sarferaz, 2019. "Identification of Financial Factors in Economic Fluctuations," The Economic Journal, Royal Economic Society, vol. 129(617), pages 311-337.
    5. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2018. "What Do Vars Tell Us About The Impact Of A Credit Supply Shock?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 625-646, May.
    6. Keating, John W., 2013. "What do we learn from Blanchard and Quah decompositions of output if aggregate demand may not be long-run neutral?," Journal of Macroeconomics, Elsevier, vol. 38(PB), pages 203-217.
    7. 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.
    8. Herwartz, Helmut & Plödt, Martin, 2014. "Sign restrictions and statistical identification under volatility breaks -- Simulation based evidence and an empirical application to monetary policy analysis," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100326, Verein für Socialpolitik / German Economic Association.
    9. Hristov, Nikolay & Hülsewig, Oliver & Wollmershäuser, Timo, 2012. "Loan supply shocks during the financial crisis: Evidence for the Euro area," Journal of International Money and Finance, Elsevier, vol. 31(3), pages 569-592.
    10. Juvenal, Luciana, 2011. "Sources of exchange rate fluctuations: Are they real or nominal?," Journal of International Money and Finance, Elsevier, vol. 30(5), pages 849-876, September.
    11. Hristov, Nikolay & Hülsewig, Oliver & Wollmershäuser, Timo, 2020. "Capital flows in the euro area and TARGET2 balances," Journal of Banking & Finance, Elsevier, vol. 113(C).
    12. 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.
    13. Mayer, Eric & Rüth, Sebastian & Scharler, Johann, 2016. "Total factor productivity and the propagation of shocks: Empirical evidence and implications for the business cycle," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 335-346.
    14. Gert Peersman & Roland Straub, 2009. "Technology Shocks And Robust Sign Restrictions In A Euro Area Svar," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(3), pages 727-750, August.
    15. Markku Lanne & Jani Luoto, 2016. "Data-Driven Inference on Sign Restrictions in Bayesian Structural Vector Autoregression," CREATES Research Papers 2016-04, Department of Economics and Business Economics, Aarhus University.
    16. Jefferson Martínez & Gabriel Rodríguez, 2020. "Macroeconomic Effects of Loan Supply Shocks: Empirical Evidence for Peru," Documentos de Trabajo / Working Papers 2020-483, Departamento de Economía - Pontificia Universidad Católica del Perú.
    17. Danne, Christian, 2015. "VARsignR: Estimating VARs using sign restrictions in R," MPRA Paper 68429, University Library of Munich, Germany.
    18. Lima, Elcyon Caiado Rocha & Maka, Alexis & Alves, Paloma, 2011. "Monetary Policy and Exchange Rate Shocks in Brazil: Sign Restrictions versus A New Hybrid Identification Approach," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.
    19. Alessio Volpicella, 2022. "SVARs Identification Through Bounds on the Forecast Error Variance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1291-1301, June.
    20. 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.

    More about this item

    Keywords

    Identification; Bayesian econometrics; sign restrictions;
    All these 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
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    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:diw:diwwpp:dp1563. 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: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.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.