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Identification of credit supply shocks in a Bayesian SVAR model of the Hungarian economy

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  • Bálint Tamási

    ()
    (Magyar Nemzeti Bank (central bank of Hungary))

  • Balázs Világi

    ()
    (Magyar Nemzeti Bank (central bank of Hungary))

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    Abstract

    Using Hungarian macroeconomic and financial data, we estimate a Bayesian structural VAR model suitable for macroprudential simulations. We identify standard macroeconomic and credit supply shocks by sign and zero restrictions. In contrast to the previous literature, different types of credit shocks are distinguished in our paper: a risk assessment and a policy shock. Our main findings are the following. First, we demonstrate that both credit supply and macroeconomic shocks explain the variance of endogenous variables at roughly similar order of magnitude. Second, it is shown that credit supply shocks do not have a dominant role in the decline of the Hungarian economy over the crisis period that started in 2008, although their contribution was non-negligible. Third, the importance of unidentified shocks increased in the crisis period.

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    File URL: http://english.mnb.hu/Root/Dokumentumtar/ENMNB/Kiadvanyok/mnben_mnbfuzetek/WP_2011_07.pdf
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    Bibliographic Info

    Paper provided by Magyar Nemzeti Bank (the central bank of Hungary) in its series MNB Working Papers with number 2011/7.

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    Length: 26 pages
    Date of creation: 2011
    Date of revision:
    Handle: RePEc:mnb:wpaper:2011/7

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    Keywords: Bayesian SVAR; zero and sign restrictions; credit supply shocks;

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    1. Zoltán M. Jakab & Viktor Várpalotai & Balázs Vonnák, 2006. "How does monetary policy affect aggregate demand? A multimodel approach for Hungary," MNB Working Papers 2006/4, Magyar Nemzeti Bank (the central bank of Hungary).
    2. Balázs Vonnák, 2010. "Risk premium shocks, monetary policy and exchange rate pass-through in the Czech Republic, Hungary and Poland," MNB Working Papers 2010/1, Magyar Nemzeti Bank (the central bank of Hungary).
    3. Zoltán Reppa, 2009. "A joint macroeconomic-yield curve model for Hungary," MNB Working Papers 2009/1, Magyar Nemzeti Bank (the central bank of Hungary).
    4. Paolo Del Giovane & Ginette Eramo & Andrea Nobili, 2010. "Disentangling demand and supply in credit developments: a survey-based analysis for Italy," Temi di discussione (Economic working papers) 764, Bank of Italy, Economic Research and International Relations Area.
    5. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    6. Thomas Helbling & M. Ayhan Kose & Christopher Otrok & Raju Huidrom, 2010. "Do Credit Shocks Matter? A Global Perspective," IMF Working Papers 10/261, International Monetary Fund.
    7. Renee Fry & Adrian Pagan, 2007. "Some Issues in Using Sign Restrictions for Identifying Structural VARs," NCER Working Paper Series 14, National Centre for Econometric Research.
    8. Roland Meeks, 2009. "Credit market shocks: evidence from corporate spreads and defaults," Working Papers 0906, Federal Reserve Bank of Dallas.
    9. Jørn Inge Halvorsen & Dag Henning Jacobsen, 2009. "Are bank lending shocks important for economic fluctuations?," Working Paper 2009/27, Norges Bank.
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