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

Author

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  • Bálint Tamási

    (Magyar Nemzeti Bank (central bank of Hungary))

  • Balázs Világi

    (Magyar Nemzeti Bank (central bank of Hungary))

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.

Suggested Citation

  • Bálint Tamási & Balázs Világi, 2011. "Identification of credit supply shocks in a Bayesian SVAR model of the Hungarian economy," MNB Working Papers 2011/7, Magyar Nemzeti Bank (Central Bank of Hungary).
  • Handle: RePEc:mnb:wpaper:2011/7
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Tomas Konecny & Oxana Babecka-Kucharcukova, 2016. "Credit Spreads and the Links between the Financial and Real Sectors in a Small Open Economy: The Case of the Czech Republic," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(4), pages 302-321, August.
    2. Michal Franta & Roman Horvath & Marek Rusnak, 2014. "Evaluating changes in the monetary transmission mechanism in the Czech Republic," Empirical Economics, Springer, vol. 46(3), pages 827-842, May.
    3. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Modelling Loans to Non-Financial Corporations within the Eurozone: A Long-Memory Approach," CESifo Working Paper Series 8674, CESifo.
    4. Romain Houssa & Jolan Mohimont & Christopher Otrok, 2015. "Sources of Business Cycles in a Low Income Country," Pacific Economic Review, Wiley Blackwell, vol. 20(1), pages 125-148, February.
    5. Ulrichs Magdalena, 2018. "Identification of Financial and Macroeconomic Shocks in a Var Model of the Polish Economy. A Stability Analysis," Economics and Business Review, Sciendo, vol. 4(1), pages 29-43, April.
    6. Dr. Gregor Bäurle & Dr. Rolf Scheufele, 2016. "Credit cycles and real activity - the Swiss case," Working Papers 2016-13, Swiss National Bank.
    7. Romain Houssa & Jolan Mohimont & Chris Otrok, 2013. "Credit Shocks and Macroeconomic Fluctuations in Emerging Markets," CESifo Working Paper Series 4281, CESifo.

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    More about this item

    Keywords

    Bayesian SVAR; zero and sign restrictions; credit supply shocks;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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